def instantiate(self):
self.name = 'tb'
self.image_size = (4, 4)
self.filter_size = (3, 3)
self.in_chn = 4
self.out_chn = 8
self.chn_per_word = 4
self.arr_x = self.out_chn
self.arr_y = self.in_chn
self.input_chn = Channel()
self.output_chn = Channel()
ifmap_glb_depth = self.image_size[0]*self.image_size[1]* \
self.in_chn//self.chn_per_word
print("ifmap glb depth:", ifmap_glb_depth)
psum_glb_depth = self.image_size[0]*self.image_size[1]* \
self.out_chn//self.chn_per_word
print("psum glb depth:", psum_glb_depth)
self.stimulus = Stimulus(self.arr_x, self.arr_y, self.chn_per_word,
self.input_chn, self.output_chn)
self.dut = WSArch(self.arr_x, self.arr_y, self.input_chn,
self.output_chn, self.chn_per_word, ifmap_glb_depth,
psum_glb_depth)
self.configuration_done = False
def instantiate(self):
self.name = 'tb'
self.input_size = 4
self.block_size = 12
self.in_sets = self.block_size // self.input_size
self.num_nonzero = 5
self.preserve_order = True
self.in_chn = Channel()
self.mid_chn = Channel()
self.out_chn = Channel()
self.converter = Converter(self.in_chn, self.mid_chn, self.input_size, self.block_size)
#self.pruner = NaivePruner(self.mid_chn,self.out_chn,self.num_nonzero, self.block_size, self.preserve_order)
self.pruner = ClusteredPruner(self.mid_chn,self.out_chn,self.num_nonzero, self.block_size, self.preserve_order)
#self.pruner = ThresholdPruner(self.mid_chn,self.out_chn,self.num_nonzero, self.block_size, self.preserve_order)
self.iterations = 10
self.iteration = 0
self.curr_set = 0
self.out_counter = 0
self.test_data = [[randint(1,5) if randint(0,3)>1 else 0\
for j in range(self.block_size)]\
for i in range(self.iterations+1)]
# send in one extra iteration to flush out last outputs
print("Stimulus:")
print("[")
for i in range(len(self.test_data)-1):
print(self.test_data[i])
print("]")
def instantiate(self):
self.name = 'tb'
self.image_size = (4, 4)
self.filter_size = (3, 3)
self.in_chn = 8
self.out_chn = 16
self.chn_per_word = 4
self.arr_x = self.out_chn // 2
self.arr_y = self.in_chn // 2
self.input_chn = Channel()
self.output_chn = Channel()
self.psum_chn = Channel(128)
self.curr_pass = 0
self.tick_counter = 0
ifmap_glb_depth = self.image_size[0]*self.image_size[1]* \
(self.in_chn//2)//self.chn_per_word
psum_glb_depth = self.image_size[0]*self.image_size[1]* \
(self.out_chn//2)//self.chn_per_word
self.stimulus = Stimulus(self.arr_x, self.arr_y, self.chn_per_word,
self.input_chn, self.output_chn,
self.psum_chn)
self.dut = WSArch(self.arr_x, self.arr_y, self.input_chn,
self.output_chn, self.chn_per_word, ifmap_glb_depth,
psum_glb_depth)
self.configuration_done = False
def instantiate(self, dram_wr_chn, noc_wr_chn, rd_chn, glb_depth,
chn_per_word):
self.dram_wr_chn = dram_wr_chn
self.noc_wr_chn = noc_wr_chn
self.rd_chn = rd_chn
self.chn_per_word = chn_per_word
self.name = 'psum_glb'
self.stat_type = 'show'
self.raw_stats = {'size': (glb_depth, chn_per_word), 'rd': 0, 'wr': 0}
self.sram = SRAM(glb_depth, chn_per_word, nports=2, name=self.name)
self.last_read = Channel(3, name='last_read')
self.filter_size = (0, 0)
self.fmap_sets = 0
self.fmap_per_iteration = 0
self.rd_set = 0
self.fmap_rd_idx = 0
self.iteration = 0
self.wr_set = 0
self.fmap_wr_idx = 0
self.wr_done = False
def instantiate(self):
self.name = 'tb'
self.image_size = (4, 4)
self.filter_size = (3, 3)
self.in_chn = 4
self.out_chn = 8
self.chn_per_word = 4
self.num_tiles = 4
self.arr_x = self.out_chn
self.arr_y = self.in_chn
self.input_chn = Channel()
self.output_chn = Channel()
self.finish_signal_chn = Channel()
self.stat_type = 'show'
self.raw_stats = {}
ifmap_glb_depth = self.image_size[0] * self.image_size[
1] * self.num_tiles * self.in_chn // self.chn_per_word
# psum_glb_depth = self.image_size[0]*self.image_size[1]*self.out_chn//self.chn_per_word
print("ifmap glb depth:", ifmap_glb_depth)
print("weight glb depth: 0")
self.stimulus = Stimulus(self.arr_x, self.arr_y, self.chn_per_word,
self.input_chn, self.output_chn,
self.finish_signal_chn)
self.dut = WSArch(self.arr_x, self.arr_y, self.input_chn,
self.output_chn, self.chn_per_word, ifmap_glb_depth)
self.configuration_done = False
def instantiate(self, wr_chn, rd_chn, glb_depth, chn_per_word):
self.wr_chn = wr_chn
self.rd_chn = rd_chn
self.chn_per_word = chn_per_word
self.glb_depth = glb_depth
self.name = 'ifmap_glb'
self.stat_type = 'show'
self.raw_stats = {
'size': (glb_depth, chn_per_word),
'ifmap_glb_rd': 0,
'ifmap_glb_wr': 0
}
self.sram = SRAM(glb_depth, chn_per_word)
self.last_read = Channel(3)
self.image_size = (0, 0)
self.filter_size = (0, 0)
self.fmap_sets = 0
self.fmap_per_iteration = 0
self.curr_set = 0
self.fmap_idx = 0
self.iteration = 0
self.wr_done = False
def instantiate(self, dram_wr_chn, noc_wr_chn, rd_chn, glb_depth,
block_size, num_nonzero):
self.dram_wr_chn = dram_wr_chn
self.noc_wr_chn = noc_wr_chn
self.rd_chn = rd_chn
self.name = 'psum_glb'
self.block_size = block_size
self.num_nonzero = num_nonzero
self.stat_type = 'show'
self.raw_stats = {'size': (glb_depth, block_size), 'rd': 0, 'wr': 0}
self.sram = SRAM(glb_depth, block_size, nports=2, dtype=np.float16)
self.last_read = Channel(3)
self.filter_size = (0, 0)
self.fmap_sets = 0
self.fmap_per_iteration = 0
self.rd_set = 0
self.fmap_rd_idx = 0
self.iteration = 0
self.wr_set = 0
self.fmap_wr_idx = 0
self.wr_done = False
def instantiate(self, wr_chn, rd_chn, glb_depth, chn_per_word):
self.wr_chn = wr_chn
self.rd_chn = rd_chn
self.chn_per_word = chn_per_word
self.name = 'ifmap_glb'
self.stat_type = 'show'
self.raw_stats = {'size' : (glb_depth, chn_per_word), 'rd': 0, 'wr': 0}
self.sram = SRAM(glb_depth, chn_per_word, name=self.name)
self.last_read = Channel(3, name='last_read')
self.image_size = (0, 0)
self.filter_size = (0, 0)
self.fmap_sets = 0
self.full_fmap_sets = 0
self.fmap_per_iteration = 0
self.curr_set = 0
self.fmap_idx = 0
self.iteration = 0
self.tile_in = 0
self.tile_out = 0
self.wr_done = False
self.task_done = True
def instantiate(self, arr_x, arr_y, chn_per_word, done_chn,
ifmap_glb_depth, psum_glb_depth, weight_glb_depth):
self.name = 'conv_tb'
self.image_size = None
self.filter_size = None
self.full_in_chn = None
self.full_out_chn = None
self.ceil_in_chn = None
self.ceil_out_chn = None
self.in_chn = arr_y
self.out_chn = arr_x
self.done_chn = done_chn
self.chn_per_word = chn_per_word
self.arr_x = self.out_chn
self.arr_y = self.in_chn
self.input_chn = Channel(name='arch_input_chn')
self.output_chn = Channel(name='arch_output_chn')
self.stimulus = Stimulus(self.arr_x, self.arr_y, self.chn_per_word,
self.input_chn, self.output_chn,
self.done_chn)
self.dut = WSArch(self.arr_x, self.arr_y, self.input_chn,
self.output_chn, self.chn_per_word, ifmap_glb_depth,
psum_glb_depth, weight_glb_depth)
class ConverterTB(Module):
def instantiate(self):
self.name = 'tb'
self.input_size = 4
self.block_size = 12
self.in_sets = self.block_size // self.input_size
self.num_nonzero = 5
self.preserve_order = True
self.in_chn = Channel()
self.mid_chn = Channel()
self.out_chn = Channel()
self.converter = Converter(self.in_chn, self.mid_chn, self.input_size, self.block_size)
#self.pruner = NaivePruner(self.mid_chn,self.out_chn,self.num_nonzero, self.block_size, self.preserve_order)
self.pruner = ClusteredPruner(self.mid_chn,self.out_chn,self.num_nonzero, self.block_size, self.preserve_order)
#self.pruner = ThresholdPruner(self.mid_chn,self.out_chn,self.num_nonzero, self.block_size, self.preserve_order)
self.iterations = 10
self.iteration = 0
self.curr_set = 0
self.out_counter = 0
self.test_data = [[randint(1,5) if randint(0,3)>1 else 0\
for j in range(self.block_size)]\
for i in range(self.iterations+1)]
# send in one extra iteration to flush out last outputs
print("Stimulus:")
print("[")
for i in range(len(self.test_data)-1):
print(self.test_data[i])
print("]")
def tick(self):
if (self.in_chn.vacancy() and not self.iteration == self.iterations+1):
imin = self.curr_set*self.input_size
imax = imin+self.input_size
data = [self.test_data[self.iteration][i] for i in range(imin, imax)]
self.in_chn.push(data)
self.curr_set += 1
if (self.curr_set == self.in_sets):
self.curr_set = 0
self.iteration += 1
if (self.out_chn.valid()):
data = self.out_chn.pop()
print(data)
#print("out_counter: ", self.out_counter)
self.out_counter += 1
if (self.out_counter == self.iterations):
raise Finish("Check manually")
def instantiate(self, image_size, filter_size, in_chn, out_chn, block_size,
ifmap, weights, bias, pruner_name, num_nonzero):
self.name = 'tb'
# if (debug):
# self.image_size = (4, 4)
# self.filter_size = (3, 3)
# self.in_chn = 2
# self.out_chn = 4
# self.block_size = 2
# self.num_nonzero = 1 #number of non-zero values in each blok, help test the correctness of the arch
# else:
# self.image_size = (16, 16)
# self.filter_size = (3, 3)
# self.in_chn = 16
# self.out_chn = 8
# self.block_size = 4
# self.num_nonzero = 4
self.image_size = image_size
self.filter_size = filter_size
self.in_chn = in_chn
self.out_chn = out_chn
self.block_size = block_size
self.num_nonzero = num_nonzero #number of non-zero values in each blok, help test the correctness of the arch
#the inputs to this specific layer
self.ifmap = ifmap
self.weights = weights
self.bias = bias
self.pruner_name = pruner_name
self.arr_y = self.out_chn
self.input_chn = Channel()
self.output_chn = Channel()
ifmap_glb_depth = (self.filter_size[1] + (self.filter_size[0]-1)*\
self.image_size[1]) * self.in_chn // self.block_size
psum_glb_depth = self.out_chn // self.block_size
weight_glb_depth = self.filter_size[0]*self.filter_size[1]* \
self.in_chn*self.out_chn//self.block_size
self.stimulus = Stimulus(self.arr_y, self.block_size, self.num_nonzero,
self.input_chn, self.output_chn,
self.pruner_name)
self.dut = OSArch(self.arr_y, self.input_chn, self.output_chn,
self.block_size, self.num_nonzero, ifmap_glb_depth,
psum_glb_depth, weight_glb_depth)
self.configuration_done = False
class ChannelTB(Module):
def instantiate(self):
self.channel = Channel(4)
self.push_count = 0
self.free_count = 0
self.test_size = 100
def tick(self):
# Print current state of the channel
c, n = [], 0
while (self.channel.valid(n)):
d = self.channel.peek(n)
assert (d == (self.free_count + n))
c.append(d)
n += 1
print("channel: %s" % c)
# Possibly push a new element
if random.random() < 0.5 and self.push_count < self.test_size and \
self.channel.vacancy():
self.channel.push(self.push_count)
print("push: %d" % self.push_count)
self.push_count += 1
# Possibly free some elements
if random.random() < 0.5 and self.free_count < self.test_size and \
n != 0:
num_free = random.randint(1, n)
self.channel.free(num_free)
self.free_count += num_free
print("free: %d" % num_free)
def instantiate(self, arch_input_chn, arr_y, block_size, num_nonzero,
pruner_name):
# PE static configuration (immutable)
#self.arr_x = arr_x
self.arr_y = arr_y
#self.chn_per_word = chn_per_word
self.block_size = block_size
self.num_nonzero = num_nonzero
self.convert_chn = Channel()
self.prune_chn = Channel()
self.arch_input_chn = arch_input_chn
# Although both InputSerializer and pruner will be pushing to arch_input_chn
# There is no conflict issue because all weights will be pushed by IS first
# then all inputs by pruner
self.converter = Converter(self.convert_chn, self.prune_chn, \
self.block_size, self.block_size)
# self.pruner = NaivePruner(self.prune_chn,self.arch_input_chn, \
# self.num_nonzero,True)
#user defined pruner for this layer, default to naive pruner
self.pruner = getattr(pruner, pruner_name)(self.prune_chn,self.arch_input_chn, \
self.num_nonzero, self.block_size, True)
self.ifmap = None
self.weights = None
self.bias = None
self.image_size = (0, 0)
self.filter_size = (0, 0)
self.ifmap_psum_done = True
self.pass_done = Reg(False)
# State Counters
self.curr_set = 0
self.curr_filter = 0
self.iteration = 0
self.fmap_idx = 0
self.curr_chn = 0
self.curr_x = 0 # run through first two dimensions of input
self.curr_y = 0
self.bias_set = 0
def instantiate(self, wr_chn, rd_chn, glb_depth, block_size):
self.wr_chn = wr_chn
self.rd_chn = rd_chn
self.name = 'weight_glb'
self.filter_size = (0, 0)
self.image_size = (0, 0)
self.wr_done = False
self.iteration = 0
self.addr = 0
self.in_chn = 0
self.out_chn = 0
#self.arr_y = 0
#self.out_sets = 0
self.block_size = block_size
self.sram = SRAM(glb_depth, block_size)
self.last_read = Channel(3)
self.stat_type = 'show'
self.raw_stats = {'size': (glb_depth, block_size), 'rd': 0, 'wr': 0}
def instantiate(self, wr_chn, rd_chn, arr_y, glb_depth, block_size,
num_nonzero):
self.wr_chn = wr_chn
self.rd_chn = rd_chn
self.arr_y = arr_y
self.block_size = block_size
self.num_nonzero = num_nonzero
self.name = 'ifmap_glb'
self.stat_type = 'show'
self.raw_stats = {'size': (glb_depth, num_nonzero), 'rd': 0, 'wr': 0}
self.sram = SRAM(glb_depth, num_nonzero * 3)
self.last_read = Channel(3)
self.glb_depth = glb_depth
self.image_size = (0, 0)
self.filter_size = (0, 0)
self.fmap_sets = 0
self.fmap_per_iteration = 0
self.curr_set = 0
self.fmap_idx = 0
self.iteration = 0
self.wr_done = False
# For managing convolution
self.curr_x = 0
self.curr_y = 0
self.curr_chn = 0
self.request_idx = 0
self.send_idx = 0
#self.curr_filt_x = 0
#self.curr_filt_y = 0
self.ifmap_done = False
self.needed_addr = 0
self.ready_to_output = False # ready to output a filter_size block of inputs
self.curr_data = [0 for i in range(3 * num_nonzero)]
self.data_idx = num_nonzero # block other operations while actively working through data
def instantiate(self, arr_x, arr_y, chn_per_word, done_chn, ifmap_glb_depth, psum_glb_depth, weight_glb_depth):
self.name = 'fc_tb'
self.arr_x = arr_x
self.arr_y = arr_y
self.chn_per_word = chn_per_word
self.batch_size = None
self.input_size = None
self.output_size = None
self.ceil_batch = None
self.ceil_output = None
self.input_chn = Channel(name='arch_input_chn')
self.output_chn = Channel(name='arch_output_chn')
self.done_chn = done_chn
self.stimulus = Stimulus(self.arr_x, self.arr_y, self.chn_per_word,
self.input_chn, self.output_chn, self.done_chn)
self.dut = OSArch(self.arr_x, self.arr_y, self.input_chn,
self.output_chn, self.chn_per_word, ifmap_glb_depth,
weight_glb_depth)
class MetaArchTB(Module):
def instantiate(self, arr_x, arr_y, chn_per_word, layers, batch_size):
self.arr_x = arr_x
self.arr_y = arr_y
self.chn_per_word = chn_per_word
self.layers = layers
self.batch_size = batch_size
self.name = 'meta'
self.started = False
self.done_chn = Channel()
self.ifmap_glb_depth = 0
self.psum_glb_depth = 0
self.weights_glb_depth = 0
use_conv = False
use_fc = False
self.conv_tb = None
self.fc_tb = None
cur_image_size = None
cur_in_chn = None
is_conv = False
num_convs = 0
num_fc = 0
for layer in self.layers:
if isinstance(layer, Conv):
if cur_image_size is None:
pass
elif cur_image_size != layer.image_size or cur_in_chn != layer.in_chn:
raise Exception('Invalid conv image size for %s: %s %s' %
(layer.name, (cur_image_size, cur_in_chn),
(layer.image_size, layer.in_chn)))
ifmap_glb_depth, psum_glb_depth, weights_glb_depth = WSArchTB.required_glb_depth(
self.arr_x, self.arr_y, self.chn_per_word,
layer.image_size, layer.filter_size, layer.in_chn,
layer.out_chn)
use_conv = True
output_shape = layer.new_shape((self.batch_size, ) +
layer.image_size +
(layer.out_chn, ))
cur_image_size = output_shape[1:3]
cur_in_chn = output_shape[3]
is_conv = True
num_convs += 1
elif isinstance(layer, FC):
if cur_image_size is None:
pass
elif not is_conv and cur_image_size != layer.input_size:
raise Exception('Invalid fc dimension transition for ' +
layer.name)
elif is_conv and cur_image_size[0] * cur_image_size[
1] * cur_in_chn != layer.input_size:
raise Exception(
'Invalid conv to fc dimension transition to ' +
layer.name)
ifmap_glb_depth, psum_glb_depth, weights_glb_depth = OSArchTB.required_glb_depth(
self.arr_x, self.arr_y, self.chn_per_word, self.batch_size,
layer.input_size, layer.output_size)
use_fc = True
_, cur_image_size = layer.new_shape(
(self.batch_size, layer.output_size))
is_conv = False
num_fc += 1
else:
raise Exception('layer not valid')
self.ifmap_glb_depth = max(self.ifmap_glb_depth, ifmap_glb_depth)
self.psum_glb_depth = max(self.psum_glb_depth, psum_glb_depth)
self.weights_glb_depth = max(self.weights_glb_depth,
weights_glb_depth)
if use_conv:
self.conv_tb = WSArchTB(self.arr_x, self.arr_y, self.chn_per_word,
self.done_chn, self.ifmap_glb_depth,
self.psum_glb_depth,
self.weights_glb_depth)
if use_fc:
self.fc_tb = OSArchTB(self.arr_x, self.arr_y, self.chn_per_word,
self.done_chn, self.ifmap_glb_depth,
self.psum_glb_depth, self.weights_glb_depth)
self.layer_step = 0
self.batch_step = 0
self.conv_inputs = [None] * self.batch_size
self.fc_input = None
self.conv_weights = [None] * num_convs
self.conv_bias = [None] * num_convs
self.fc_weights = [None] * num_fc
self.fc_bias = [None] * num_fc
self.cur_conv = 0
self.cur_fc = 0
def tick(self):
if not self.started or self.done_chn.valid():
self.started = True
old_layer = self.layers[self.layer_step]
if self.done_chn.valid():
valid = self.done_chn.pop()
if not valid:
raise Finish('Validation Failed')
if isinstance(old_layer, Conv):
self.conv_inputs[
self.batch_step] = self.conv_tb.get_output()
self.batch_step += 1
if self.batch_step == self.batch_size:
self.conv_inputs = [
batch for batch in old_layer.activation(
np.array(self.conv_inputs))
]
self.batch_step = 0
self.layer_step += 1
self.cur_conv += 1
else:
self.fc_input = self.fc_tb.get_output()
self.fc_input = old_layer.activation(self.fc_input)
self.layer_step += 1
self.cur_fc += 1
if self.layer_step == len(self.layers):
raise Finish('Success')
layer = self.layers[self.layer_step]
# handle conv to fc transition
if isinstance(
layer, FC
) and self.fc_input is None and self.conv_inputs[0] is not None:
if self.name != None:
self.output_file.write("FC MODE\n")
self.fc_input = np.zeros(
(self.batch_size, layer.input_size)).astype(np.int64)
for i in range(self.batch_size):
self.fc_input[i] = self.conv_inputs[i].reshape(
layer.input_size)
if isinstance(layer, Conv):
if self.name != None:
self.output_file.write("CONV MODE\n")
if self.conv_inputs[self.batch_step] is None:
_, weights, bias = self.conv_tb.configure(
layer.image_size, layer.filter_size, layer.in_chn,
layer.out_chn)
self.conv_weights[self.cur_conv] = weights
self.conv_bias[self.cur_conv] = bias
elif self.conv_weights[
self.cur_conv] is None or self.conv_bias[
self.cur_conv] is None:
weights, bias = self.conv_tb.configure_fixed_image(
self.conv_inputs[self.batch_step], layer.filter_size,
layer.in_chn, layer.out_chn)
self.conv_weights[self.cur_conv] = weights
self.conv_bias[self.cur_conv] = bias
else:
self.conv_tb.configure_fixed(
self.conv_inputs[self.batch_step],
self.conv_weights[self.cur_conv],
self.conv_bias[self.cur_conv])
elif isinstance(layer, FC):
if self.fc_input is None:
_, weights, bias = self.fc_tb.configure(
self.batch_size, layer.input_size, layer.output_size)
self.fc_weights[self.cur_fc] = weights
self.fc_bias[self.cur_fc] = bias
elif self.fc_weights[self.cur_fc] is None or self.fc_bias[
self.cur_fc] is None:
weights, bias = self.fc_tb.configure_fixed_image(
self.fc_input, layer.output_size)
self.fc_weights[self.cur_fc] = weights
self.fc_bias[self.cur_fc] = bias
else:
self.fc_tb.configure_fixed(self.fc_input,
self.fc_weights[self.cur_fc],
self.fc_bias[self.cur_fc])
else:
raise Exception('layer not valid')
class InputSerializer(Module):
def instantiate(self, arch_input_chn, arr_y, block_size, num_nonzero,
pruner_name):
# PE static configuration (immutable)
#self.arr_x = arr_x
self.arr_y = arr_y
#self.chn_per_word = chn_per_word
self.block_size = block_size
self.num_nonzero = num_nonzero
self.convert_chn = Channel()
self.prune_chn = Channel()
self.arch_input_chn = arch_input_chn
# Although both InputSerializer and pruner will be pushing to arch_input_chn
# There is no conflict issue because all weights will be pushed by IS first
# then all inputs by pruner
self.converter = Converter(self.convert_chn, self.prune_chn, \
self.block_size, self.block_size)
# self.pruner = NaivePruner(self.prune_chn,self.arch_input_chn, \
# self.num_nonzero,True)
#user defined pruner for this layer, default to naive pruner
self.pruner = getattr(pruner, pruner_name)(self.prune_chn,self.arch_input_chn, \
self.num_nonzero, self.block_size, True)
self.ifmap = None
self.weights = None
self.bias = None
self.image_size = (0, 0)
self.filter_size = (0, 0)
self.ifmap_psum_done = True
self.pass_done = Reg(False)
# State Counters
self.curr_set = 0
self.curr_filter = 0
self.iteration = 0
self.fmap_idx = 0
self.curr_chn = 0
self.curr_x = 0 # run through first two dimensions of input
self.curr_y = 0
self.bias_set = 0
#self.send_bias = False
def configure(self, ifmap, weights, bias, in_chn, out_chn, image_size,
filter_size):
self.ifmap = ifmap
self.weights = weights
self.bias = bias
self.in_chn = in_chn
self.out_chn = out_chn
self.image_size = image_size
self.filter_size = filter_size
self.ifmap_psum_done = False
self.weights_done = False
self.pass_done.wr(False)
# State Counters
self.curr_set = 0
self.curr_filter = 0
self.iteration = 0
self.curr_chn = 0
self.curr_x = 0 # run through first two dimensions of input
self.curr_y = 0
self.bias_set = 0
#self.send_bias = False
def tick(self):
if self.pass_done.rd():
return
if self.ifmap_psum_done:
if self.convert_chn.vacancy():
data = np.zeros(self.block_size)
self.convert_chn.push(data)
return
in_sets = self.in_chn // self.block_size
out_sets = self.out_chn // self.block_size
num_iteration = self.filter_size[0] * self.filter_size[1]
# read and hold all weights at the beginning for ease of implementation
if not self.weights_done:
f_x = self.iteration // self.filter_size[0]
f_y = self.iteration % self.filter_size[0]
# Push filters to PE columns. (PE is responsible for pop)
if self.arch_input_chn.vacancy(
) and self.iteration < num_iteration:
cmin = self.curr_filter * self.block_size
cmax = cmin + self.block_size
data = np.array([self.weights[f_x, f_y, self.curr_chn, c] \
for c in range(cmin, cmax) ])
#print("{},{},{},{}-{}".format(f_x,f_y,self.curr_chn,cmin,cmax))
#print(data)
self.arch_input_chn.push(
data) # Gives groups of four along num_filters axis
self.curr_filter += 1
if (self.curr_filter == out_sets
): # Loop through blocks of filters
self.curr_filter = 0
self.curr_chn += 1
if (self.curr_chn == self.in_chn): # Loop through channels
self.curr_chn = 0
self.iteration += 1
if (self.iteration == num_iteration
): # Loop through 2D filter support
self.iteration = 0
#print("Weights done")
self.weights_done = True
elif self.arch_input_chn.vacancy() and self.bias_set < out_sets:
cmin = self.bias_set * self.block_size
cmax = cmin + self.block_size
data = np.array([self.bias[c] for c in range(cmin, cmax)])
#print("bias (input serializer):")
#print(data)
self.arch_input_chn.push(data)
self.bias_set += 1
elif not self.ifmap_psum_done:
if self.convert_chn.vacancy():
cmin = self.curr_set * self.block_size
cmax = cmin + self.block_size
#xmin = x
#xmax = x+self.arr_x
# Write ifmap to glb
#data = np.array([ self.ifmap[x, self.curr_y, self.curr_chn] for x in range(xmin, xmax) ])
data = np.array([
self.ifmap[self.curr_x, self.curr_y, c]
for c in range(cmin, cmax)
])
#print("{},{},{}-{}".format(self.curr_x, self.curr_y, cmin, cmax))
#print(data)
self.curr_set += 1
if (self.curr_set == in_sets):
self.curr_set = 0
self.curr_y += 1
if (self.curr_y == self.image_size[1]):
self.curr_y = 0
self.curr_x += 1
self.convert_chn.push(data)
if (self.curr_x == self.image_size[0]):
self.curr_x = 0
self.ifmap_psum_done = True
class IFMapGLB(Module):
def instantiate(self, wr_chn, rd_chn, glb_depth, chn_per_word):
self.wr_chn = wr_chn
self.rd_chn = rd_chn
self.chn_per_word = chn_per_word
self.name = 'ifmap_glb'
self.stat_type = 'show'
self.raw_stats = {'size': (glb_depth, chn_per_word), 'rd': 0, 'wr': 0}
self.sram = SRAM(glb_depth, chn_per_word, name=self.name)
self.last_read = Channel(3, name='last_read')
self.image_size = (0, 0)
self.filter_size = (0, 0)
self.fmap_sets = 0
self.fmap_per_iteration = 0
self.curr_set = 0
self.fmap_idx = 0
self.iteration = 0
self.wr_done = False
def configure(self, image_size, filter_size, fmap_sets,
fmap_per_iteration):
self.wr_done = False
self.image_size = image_size
self.filter_size = filter_size
self.fmap_sets = fmap_sets
self.fmap_per_iteration = fmap_per_iteration
def tick(self):
# self.iteration is which weight we are currently using
# It's weight stationary so we fully use a set of filter weights
# before continuing on.
# (first weight in each filter, second weight in each filter, etc...)
num_iteration = self.filter_size[0] * self.filter_size[1]
offset_x = (self.filter_size[0] - 1) // 2
offset_y = (self.filter_size[1] - 1) // 2
filter_x = self.iteration % self.filter_size[0] - offset_x
filter_y = self.iteration // self.filter_size[0] - offset_y
# This is the first tick since initializing
# INITIALIZATION CODE
# Write all ifmaps and psums? to sram
if not self.wr_done:
# Write to GLB
if self.wr_chn.valid():
data = self.wr_chn.pop()
# print "ifmap_glb wr"
self.raw_stats['wr'] += 1
# Write ifmap to glb
# print "ifmap_to_glb: ", in_sets, self.fmap_idx, self.curr_set
addr = self.fmap_sets * self.fmap_idx + self.curr_set
self.curr_set += 1
self.sram.request(WR, addr, data)
if self.curr_set == self.fmap_sets:
self.curr_set = 0
self.fmap_idx += 1
if self.fmap_idx == self.fmap_per_iteration:
# Done initializing ifmaps and psums
# self.sram.dump()
self.fmap_idx = 0
self.wr_done = True
else:
# Read from GLB and deal with SRAM latency
if self.rd_chn.vacancy(1) and self.iteration < num_iteration:
fmap_x = self.fmap_idx % self.image_size[0]
fmap_y = self.fmap_idx // self.image_size[0]
ifmap_x, ifmap_y = (fmap_x + filter_x, fmap_y + filter_y)
if (ifmap_x < 0) or (ifmap_x >= self.image_size[0]) or \
(ifmap_y < 0) or (ifmap_y >= self.image_size[1]):
# print "ifmap req zero", self.iteration, self.fmap_idx
self.last_read.push(True)
else:
fmap_idx = (ifmap_y * self.image_size[0]) + ifmap_x
addr = self.fmap_sets * fmap_idx + self.curr_set
# print "ifmap req glb", self.iteration, self.fmap_idx
self.sram.request(RD, addr)
self.last_read.push(False)
self.curr_set += 1
if self.curr_set == self.fmap_sets:
self.curr_set = 0
self.fmap_idx += 1
if self.fmap_idx == self.fmap_per_iteration:
self.fmap_idx = 0
self.iteration += 1
# Process the last read sent to the GLB SRAM
if self.last_read.valid():
is_zero = self.last_read.pop()
# self.raw_stats['rd'] += 1
data = [0]*self.chn_per_word if is_zero else \
[e for e in self.sram.response()]
# print "ifmap rd glb", data
self.rd_chn.push(data)
self.raw_stats['rd'] += 1
class WeightsGLB(Module):
def instantiate(self, wr_chn, rd_chn, glb_depth, chn_per_word):
self.wr_chn = wr_chn
self.rd_chn = rd_chn
self.chn_per_word = chn_per_word
self.name = 'weight_glb'
self.stat_type = 'show'
self.raw_stats = {'size' : (glb_depth, chn_per_word), 'rd': 0, 'wr': 0}
self.sram = SRAM(glb_depth, chn_per_word, name=self.name)
self.last_read = Channel(3, name='last_read')
self.filter_size = (0, 0)
self.in_sets = 0
self.out_sets = 0
self.curr_set = 0
self.fmap_idx = 0
self.iteration = 0
self.tile = 0
self.wr_done = False
def configure(self, filter_size, in_sets, out_sets):
self.wr_done = False
self.filter_size = filter_size
self.in_sets = in_sets
self.out_sets = out_sets
self.tile = 0
self.stuff = []
def tick(self):
num_iteration = self.filter_size[0]*self.filter_size[1]
if not self.wr_done:
# Write to GLB
if self.wr_chn.valid():
data = self.wr_chn.pop()
# print "ifmap_glb wr"
# Write ifmap to glb
# print "ifmap_to_glb: ", in_sets, self.fmap_idx, self.curr_set
addr = self.in_sets*(self.out_sets*self.iteration+self.fmap_idx) + self.curr_set
self.stuff.append(data)
self.curr_set += 1
self.sram.request(WR, addr, data)
self.raw_stats['wr'] += len(data)
if self.curr_set == self.in_sets:
self.curr_set = 0
self.fmap_idx += 1
if self.fmap_idx == self.out_sets:
# Done initializing ifmaps and psums
# self.sram.dump()
self.fmap_idx = 0
self.iteration += 1
if self.iteration == num_iteration:
self.iteration = 0
self.wr_done = True
else:
did_read = False
# Read from GLB and deal with SRAM latency
if self.rd_chn.vacancy(1) and self.iteration < num_iteration:
addr = self.in_sets*(self.out_sets*self.iteration+self.fmap_idx) + self.curr_set
# print "ifmap req glb", self.iteration, self.fmap_idx
self.sram.request(RD, addr)
self.raw_stats['rd'] += self.chn_per_word
self.last_read.push(False)
did_read = True
self.curr_set += 1
if self.curr_set == self.in_sets:
self.curr_set = 0
self.fmap_idx += 1
if self.fmap_idx == self.out_sets:
self.fmap_idx = 0
self.iteration += 1
# Process the last read sent to the GLB SRAM
if self.last_read.valid():
is_zero = self.last_read.pop()
data = [0]*self.chn_per_word if is_zero else \
[e for e in self.sram.response()]
# print "ifmap rd glb", data
self.rd_chn.push(data)
elif not did_read:
if self.iteration == num_iteration:
self.iteration = 0
self.wr_done = False
def instantiate(self, arr_x, arr_y, input_chn, output_chn, chn_per_word,
ifmap_glb_depth, psum_glb_depth):
# PE static configuration (immutable)
self.name = 'chip'
self.arr_x = arr_x
self.arr_y = arr_y
self.chn_per_word = chn_per_word
self.stat_type = 'show'
# Instantiate DRAM IO channels
self.input_chn = input_chn
self.output_chn = output_chn
# Instantiate input deserializer and output serializer
self.ifmap_wr_chn = Channel(name="ifmap_wr_chn")
self.psum_wr_chn = Channel(name="psum_wr_chn")
self.weights_wr_chn = Channel(name="weights_wr_chn")
self.deserializer = InputDeserializer(self.input_chn,
self.ifmap_wr_chn,
self.weights_wr_chn,
self.psum_wr_chn, arr_x, arr_y,
chn_per_word)
self.psum_output_chn = Channel(name="psum_output_chn")
self.serializer = OutputSerializer(self.output_chn,
self.psum_output_chn)
# Instantiate GLB and GLB channels
self.ifmap_rd_chn = Channel(3, name='ifmap_rd_chn')
self.ifmap_glb = IFMapGLB(self.ifmap_wr_chn, self.ifmap_rd_chn,
ifmap_glb_depth, chn_per_word)
self.psum_rd_chn = Channel(3, name='psum_rd_chn')
self.psum_noc_wr_chn = Channel(name='psum_noc_wr_chn')
self.psum_glb = PSumGLB(self.psum_wr_chn, self.psum_noc_wr_chn,
self.psum_rd_chn, psum_glb_depth, chn_per_word)
self.weights_rd_chn = Channel(name='weights_rd_chn')
self.weights_glb = WeightsGLB(self.weights_wr_chn, self.weights_rd_chn)
# PE Array and local channel declaration
self.pe_array = ModuleList()
self.pe_ifmap_chns = ModuleList()
self.pe_filter_chns = ModuleList()
self.pe_psum_chns = ModuleList()
self.pe_psum_chns.append(ModuleList())
for x in range(self.arr_x):
self.pe_psum_chns[0].append(
Channel(32, name='pe_psum_chns_{}_{}'.format(x, 0)))
# Actual array instantiation
for y in range(self.arr_y):
self.pe_array.append(ModuleList())
self.pe_ifmap_chns.append(ModuleList())
self.pe_filter_chns.append(ModuleList())
self.pe_psum_chns.append(ModuleList())
for x in range(self.arr_x):
self.pe_ifmap_chns[y].append(
Channel(32, name='pe_ifmap_chns_{}_{}'.format(x, y)))
self.pe_filter_chns[y].append(
Channel(32, name='pe_filter_chns_{}_{}'.format(x, y)))
self.pe_psum_chns[y + 1].append(
Channel(32, name='pe_psum_chns_{}_{}'.format(x, y)))
self.pe_array[y].append(
PE(x, y, self.pe_ifmap_chns[y][x],
self.pe_filter_chns[y][x], self.pe_psum_chns[y][x],
self.pe_psum_chns[y + 1][x]))
# Setup NoC to deliver weights, ifmaps and psums
self.filter_noc = WeightsNoC(self.weights_rd_chn, self.pe_filter_chns,
self.chn_per_word)
self.ifmap_noc = IFMapNoC(self.ifmap_rd_chn, self.pe_ifmap_chns,
self.arr_x, self.chn_per_word)
self.psum_rd_noc = PSumRdNoC(self.psum_rd_chn, self.pe_psum_chns[0],
self.chn_per_word)
self.psum_wr_noc = PSumWrNoC(self.pe_psum_chns[-1],
self.psum_noc_wr_chn,
self.psum_output_chn, self.chn_per_word)
class IFMapGLB(Module):
def instantiate(self, wr_chn, rd_chn, glb_depth, chn_per_word):
self.wr_chn = wr_chn
self.rd_chn = rd_chn
self.chn_per_word = chn_per_word
self.name = 'ifmap_glb'
self.stat_type = 'show'
self.raw_stats = {'size' : (glb_depth, chn_per_word), 'ifmap_glb_rd': 0, 'ifmap_glb_wr': 0}
self.sram = SRAM(glb_depth, chn_per_word)
self.last_read = Channel(3)
self.image_size = (0, 0)
self.filter_size = (0, 0)
self.fmap_sets = 0
self.fmap_per_iteration = 0
self.curr_set = 0
self.fmap_idx = 0
self.iteration = 0
self.wr_done = False
def configure(self, image_size, filter_size, fmap_sets, fmap_per_iteration):
self.wr_done = False
self.image_size = image_size
self.filter_size = filter_size
self.fmap_sets = fmap_sets
self.fmap_per_iteration = fmap_per_iteration
self.read_ctr = 0
def tick(self):
num_iteration = self.filter_size[0]*self.filter_size[1]
offset_x = (self.filter_size[0] - 1)//2
offset_y = (self.filter_size[1] - 1)//2
filter_x = self.iteration % self.filter_size[0] - offset_x
filter_y = self.iteration // self.filter_size[0] - offset_y
if not self.wr_done:
# Write to GLB
if self.wr_chn.valid():
data = self.wr_chn.pop()
self.raw_stats['ifmap_glb_wr'] += len(data)
# print "ifmap_glb wr"
# Write ifmap to glb
addr = self.fmap_sets*self.fmap_idx + self.curr_set
# print("ifmap_to_glb: fmap idx, curr set, addr ", self.fmap_idx, self.curr_set, addr)
self.curr_set += 1
self.sram.request(WR, addr, data)
if self.curr_set == self.fmap_sets:
self.curr_set = 0
self.fmap_idx += 1
if self.fmap_idx == self.fmap_per_iteration:
# Done initializing ifmaps and psums
# self.sram.dump()
self.fmap_idx = 0
self.wr_done = True
else:
# Read from GLB and deal with SRAM latency
if self.rd_chn.vacancy(1) and self.iteration < num_iteration:
self.read_ctr += 1
#print("ifmap glb read ctr ", self.read_ctr)
fmap_x = self.fmap_idx % self.image_size[0]
fmap_y = self.fmap_idx // self.image_size[0]
ifmap_x, ifmap_y = (fmap_x + filter_x, fmap_y + filter_y)
if (ifmap_x < 0) or (ifmap_x >= self.image_size[0]) or \
(ifmap_y < 0) or (ifmap_y >= self.image_size[1]):
# print("ifmap req zero: iter, fmap idx ", self.iteration, self.fmap_idx)
self.last_read.push(True)
else:
fmap_idx = (ifmap_y*self.image_size[0]) + ifmap_x
addr = self.fmap_sets*fmap_idx + self.curr_set
# print("addr fmap idx, addr: ", fmap_idx, addr)
#print("ifmap req glb: iter, fmap idx, addr ", self.iteration, self.fmap_idx, addr)
self.sram.request(RD, addr)
self.last_read.push(False)
self.curr_set += 1
if self.curr_set == self.fmap_sets:
self.curr_set = 0
self.fmap_idx += 1
if self.fmap_idx == self.fmap_per_iteration:
# print("fmap idx, fmap per iter: ", self.fmap_idx, self.fmap_per_iteration)
self.fmap_idx = 0
self.iteration += 1
# Process the last read sent to the GLB SRAM
if self.last_read.valid():
is_zero = self.last_read.pop()
data = [0]*self.chn_per_word if is_zero else \
[e for e in self.sram.response()]
#print("ifmap rd glb", data, self.iteration)
self.rd_chn.push(data)
self.raw_stats['ifmap_glb_rd'] += len(data)
def instantiate(self):
self.channel = Channel(4)
self.push_count = 0
self.free_count = 0
self.test_size = 100
def instantiate(self, arr_y,
input_chn, output_chn,
block_size, num_nonzero,
ifmap_glb_depth, psum_glb_depth, weight_glb_depth):
# PE static configuration (immutable)
self.name = 'chip'
#self.arr_x = arr_x
self.arr_y = arr_y
self.block_size = block_size
self.num_nonzero = num_nonzero
self.stat_type = 'show'
# Instantiate DRAM IO channels
self.input_chn = input_chn
self.output_chn = output_chn
# Instantiate input deserializer and output serializer
self.ifmap_wr_chn = Channel()
self.psum_wr_chn = Channel()
self.weights_wr_chn = Channel()
self.deserializer = InputDeserializer(self.input_chn, self.ifmap_wr_chn,
self.weights_wr_chn, self.psum_wr_chn, arr_y,
block_size, num_nonzero)
self.psum_output_chn = Channel()
self.serializer = OutputSerializer(self.output_chn, self.psum_output_chn)
# Instantiate GLB and GLB channels
self.ifmap_rd_chn = Channel(3)
#self.ifmap_glb = IFMapGLB(self.ifmap_wr_chn, self.ifmap_rd_chn, arr_y,
# ifmap_glb_depth, block_size, num_nonzero)
self.psum_rd_chn = Channel(3)
self.psum_noc_wr_chn = Channel()
self.psum_glb = PSumGLB(self.psum_wr_chn, self.psum_noc_wr_chn, self.psum_rd_chn,
psum_glb_depth, block_size, num_nonzero)
self.weights_rd_chn = Channel()
#self.weights_glb = WeightsGLB(self.weights_wr_chn, self.weights_rd_chn, weight_glb_depth, block_size)
self.ifmap_weights_glb = IFMapWeightsGLB(self.ifmap_wr_chn, self.ifmap_rd_chn,\
self.weights_wr_chn, self.weights_rd_chn, arr_y, ifmap_glb_depth,\
weight_glb_depth, block_size, num_nonzero)
# PE Array and local channel declaration
self.pe_array = ModuleList()
self.pe_ifmap_chns = ModuleList()
self.pe_filter_chns = ModuleList()
self.pe_psum_in_chns = ModuleList()
self.pe_psum_out_chns = ModuleList()
# Actual array instantiation
for y in range(self.arr_y):
self.pe_array.append(ModuleList())
self.pe_ifmap_chns.append(ModuleList())
self.pe_filter_chns.append(ModuleList())
self.pe_psum_in_chns.append(ModuleList())
self.pe_psum_out_chns.append(ModuleList())
for x in range(1):
self.pe_ifmap_chns[y].append(Channel(32))
self.pe_filter_chns[y].append(Channel(32))
self.pe_psum_in_chns[y].append(Channel(32))
self.pe_psum_out_chns[y].append(Channel(32))
self.pe_array[y].append(
PE(x, y,
self.pe_ifmap_chns[y][x],
self.pe_filter_chns[y][x],
self.pe_psum_in_chns[y][x],
self.pe_psum_out_chns[y][x]
)
)
# Setup NoC to deliver weights, ifmaps and psums
self.filter_noc = WeightsNoC(self.weights_rd_chn, self.pe_filter_chns, block_size)
self.ifmap_noc = IFMapNoC(self.ifmap_rd_chn, self.pe_ifmap_chns)
self.psum_rd_noc = PSumRdNoC(self.psum_rd_chn, self.pe_psum_in_chns, self.arr_y, block_size)
self.psum_wr_noc = PSumWrNoC(self.pe_psum_out_chns, self.psum_noc_wr_chn, self.psum_output_chn, self.arr_y, block_size)
def instantiate(self, arr_x, arr_y, chn_per_word, layers, batch_size):
self.arr_x = arr_x
self.arr_y = arr_y
self.chn_per_word = chn_per_word
self.layers = layers
self.batch_size = batch_size
self.name = 'meta'
self.started = False
self.done_chn = Channel()
self.ifmap_glb_depth = 0
self.psum_glb_depth = 0
self.weights_glb_depth = 0
use_conv = False
use_fc = False
self.conv_tb = None
self.fc_tb = None
cur_image_size = None
cur_in_chn = None
is_conv = False
num_convs = 0
num_fc = 0
for layer in self.layers:
if isinstance(layer, Conv):
if cur_image_size is None:
pass
elif cur_image_size != layer.image_size or cur_in_chn != layer.in_chn:
raise Exception('Invalid conv image size for %s: %s %s' %
(layer.name, (cur_image_size, cur_in_chn),
(layer.image_size, layer.in_chn)))
ifmap_glb_depth, psum_glb_depth, weights_glb_depth = WSArchTB.required_glb_depth(
self.arr_x, self.arr_y, self.chn_per_word,
layer.image_size, layer.filter_size, layer.in_chn,
layer.out_chn)
use_conv = True
output_shape = layer.new_shape((self.batch_size, ) +
layer.image_size +
(layer.out_chn, ))
cur_image_size = output_shape[1:3]
cur_in_chn = output_shape[3]
is_conv = True
num_convs += 1
elif isinstance(layer, FC):
if cur_image_size is None:
pass
elif not is_conv and cur_image_size != layer.input_size:
raise Exception('Invalid fc dimension transition for ' +
layer.name)
elif is_conv and cur_image_size[0] * cur_image_size[
1] * cur_in_chn != layer.input_size:
raise Exception(
'Invalid conv to fc dimension transition to ' +
layer.name)
ifmap_glb_depth, psum_glb_depth, weights_glb_depth = OSArchTB.required_glb_depth(
self.arr_x, self.arr_y, self.chn_per_word, self.batch_size,
layer.input_size, layer.output_size)
use_fc = True
_, cur_image_size = layer.new_shape(
(self.batch_size, layer.output_size))
is_conv = False
num_fc += 1
else:
raise Exception('layer not valid')
self.ifmap_glb_depth = max(self.ifmap_glb_depth, ifmap_glb_depth)
self.psum_glb_depth = max(self.psum_glb_depth, psum_glb_depth)
self.weights_glb_depth = max(self.weights_glb_depth,
weights_glb_depth)
if use_conv:
self.conv_tb = WSArchTB(self.arr_x, self.arr_y, self.chn_per_word,
self.done_chn, self.ifmap_glb_depth,
self.psum_glb_depth,
self.weights_glb_depth)
if use_fc:
self.fc_tb = OSArchTB(self.arr_x, self.arr_y, self.chn_per_word,
self.done_chn, self.ifmap_glb_depth,
self.psum_glb_depth, self.weights_glb_depth)
self.layer_step = 0
self.batch_step = 0
self.conv_inputs = [None] * self.batch_size
self.fc_input = None
self.conv_weights = [None] * num_convs
self.conv_bias = [None] * num_convs
self.fc_weights = [None] * num_fc
self.fc_bias = [None] * num_fc
self.cur_conv = 0
self.cur_fc = 0
class PSumGLB(Module):
def instantiate(self, dram_wr_chn, noc_wr_chn, rd_chn, glb_depth, chn_per_word):
self.dram_wr_chn = dram_wr_chn
self.noc_wr_chn = noc_wr_chn
self.rd_chn = rd_chn
self.chn_per_word = chn_per_word
self.name = 'psum_glb'
self.stat_type = 'show'
self.raw_stats = {'size' : (glb_depth, chn_per_word), 'psum_glb_rd': 0, 'psum_glb_wr': 0}
self.sram = SRAM(glb_depth, chn_per_word, nports=2)
self.last_read = Channel(3)
self.filter_size = (0, 0)
self.fmap_sets = 0
self.fmap_per_iteration = 0
self.rd_set = 0
self.fmap_rd_idx = 0
self.iteration = 0
self.wr_set = 0
self.fmap_wr_idx = 0
self.wr_done = False
def configure(self, filter_size, fmap_sets, fmap_per_iteration):
self.wr_done = False
self.filter_size = filter_size
self.fmap_sets = fmap_sets
self.fmap_per_iteration = fmap_per_iteration
self.rd_set = 0
self.fmap_rd_idx = 0
self.iteration = 0
self.wr_set = 0
self.fmap_wr_idx = 0
self.wr_done = False
def tick(self):
num_iteration = self.filter_size[0]*self.filter_size[1]
if not self.wr_done:
# Write to GLB
if self.dram_wr_chn.valid():
data = self.dram_wr_chn.pop()
self.raw_stats['psum_glb_wr'] += len(data)
# print "psum_glb wr"
# Write ifmap to glb
addr = self.fmap_sets*self.fmap_wr_idx + self.wr_set
self.wr_set += 1
self.sram.request(WR, addr, data, port=1)
if self.wr_set == self.fmap_sets:
self.wr_set = 0
self.fmap_wr_idx += 1
if self.fmap_wr_idx == self.fmap_per_iteration:
# Done initializing ifmaps and psums
# self.sram.dump()
self.fmap_wr_idx = 0
self.wr_done = True
#print ("psum orig write, fmap_sets, fmap_wr_idx, wr_set, addr, data: ",self.fmap_sets, self.fmap_wr_idx, self.wr_set, addr, data)
else:
# Read from GLB and deal with SRAM latency
# print self.rd_chn.vacancy(1), self.rd_chn.rd_ptr.rd(), self.rd_chn.wr_ptr.rd()
if self.rd_chn.vacancy(1) and self.iteration < num_iteration:
addr = self.fmap_sets*self.fmap_rd_idx + self.rd_set
#print("psum req glb", self.iteration, self.fmap_rd_idx, self.rd_set)
self.sram.request(RD, addr, port=0)
self.last_read.push(False)
self.rd_set += 1
if self.rd_set == self.fmap_sets:
self.rd_set = 0
self.fmap_rd_idx += 1
if self.fmap_rd_idx == self.fmap_per_iteration:
self.fmap_rd_idx = 0
self.iteration += 1
# Process the last read sent to the GLB SRAM
if self.last_read.valid():
is_zero = self.last_read.pop()
data = [0]*self.chn_per_word if is_zero else \
[e for e in self.sram.response()]
self.rd_chn.push(data)
self.raw_stats['psum_glb_rd'] += len(data)
#print("psum rd glb: data", data)
if self.noc_wr_chn.valid():
data = self.noc_wr_chn.pop()
#print("psum_to_glb: ", self.fmap_wr_idx, self.wr_set, data)
self.raw_stats['psum_glb_wr'] += len(data)
addr = self.fmap_sets*self.fmap_wr_idx + self.wr_set
#print("noc psum wr glb", self.fmap_wr_idx, self.wr_set, data)
self.wr_set += 1
self.sram.request(WR, addr, data, port=1)
if self.wr_set == self.fmap_sets:
self.wr_set = 0
self.fmap_wr_idx += 1
if self.fmap_wr_idx == self.fmap_per_iteration:
# Done initializing ifmaps and psums
#self.sram.dump()
self.fmap_wr_idx = 0
class IFMapGLB(Module):
def instantiate(self, wr_chn, rd_chn, glb_depth, chn_per_word):
self.wr_chn = wr_chn
self.rd_chn = rd_chn
self.chn_per_word = chn_per_word
self.glb_depth = glb_depth
self.name = 'ifmap_glb'
self.stat_type = 'show'
self.raw_stats = {
'size': (glb_depth, chn_per_word),
'ifmap_glb_rd': 0,
'ifmap_glb_wr': 0
}
self.sram = SRAM(glb_depth, chn_per_word)
self.last_read = Channel(3)
self.image_size = (0, 0)
self.filter_size = (0, 0)
self.fmap_sets = 0
self.fmap_per_iteration = 0
self.curr_set = 0
self.fmap_idx = 0
self.iteration = 0
self.wr_done = False
def configure(self, image_size, filter_size, fmap_sets,
fmap_per_iteration):
self.wr_done = False
self.image_size = image_size
self.filter_size = filter_size
self.fmap_sets = fmap_sets
self.fmap_per_iteration = fmap_per_iteration
self.curr_tile = 0
self.num_tiles = 4
self.addr = 0
print("ifmap glb_size: ", self.glb_depth)
def tick(self):
num_iteration = self.filter_size[0] * self.filter_size[1]
offset_x = (self.filter_size[0] - 1) // 2
offset_y = (self.filter_size[1] - 1) // 2
filter_x = self.iteration % self.filter_size[0] - offset_x
filter_y = self.iteration // self.filter_size[0] - offset_y
if not self.wr_done:
# Write to GLB
if self.wr_chn.valid():
data = self.wr_chn.pop()
self.raw_stats['ifmap_glb_wr'] += len(data)
# print "ifmap_glb wr"
# Write ifmap to glb
addr = self.fmap_sets * self.curr_tile + self.curr_set + self.fmap_idx * self.num_tiles
#print ("ifmap_to_glb: ", self.curr_tile, self.fmap_idx, addr)
self.curr_set += 1
self.sram.request(WR, addr, data)
if self.curr_set == self.fmap_sets:
self.curr_set = 0
self.curr_tile += 1
if self.curr_tile == self.num_tiles:
# Done initializing ifmaps and psums
# self.sram.dump()
self.curr_tile = 0
self.fmap_idx += 1
if self.fmap_idx == self.fmap_per_iteration:
self.wr_done = True
else:
if self.rd_chn.vacancy(1) and self.addr < self.glb_depth:
# Read from GLB and deal with SRAM latency
self.sram.request(RD, self.addr)
#print ("read_ifmap_glb: ", self.addr)
self.addr += 1
self.last_read.push(False)
# Process the last read sent to the GLB SRAM
if self.last_read.valid():
#print ("ifmap_glb_to_noc")
is_zero = self.last_read.pop()
data = [e for e in self.sram.response()]
# print "ifmap rd glb", data
self.rd_chn.push(data)
self.raw_stats['ifmap_glb_rd'] += len(data)
class PSumGLB(Module):
def instantiate(self, dram_wr_chn, noc_wr_chn, rd_chn, glb_depth,
chn_per_word):
self.dram_wr_chn = dram_wr_chn
self.noc_wr_chn = noc_wr_chn
self.rd_chn = rd_chn
self.chn_per_word = chn_per_word
self.name = 'psum_glb'
self.stat_type = 'show'
self.raw_stats = {'size': (glb_depth, chn_per_word), 'rd': 0, 'wr': 0}
self.sram = SRAM(glb_depth, chn_per_word, nports=2, name=self.name)
self.last_read = Channel(3, name='last_read')
self.filter_size = (0, 0)
self.fmap_sets = 0
self.fmap_per_iteration = 0
self.rd_set = 0
self.fmap_rd_idx = 0
self.iteration = 0
self.wr_set = 0
self.fmap_wr_idx = 0
self.wr_done = False
def configure(self, filter_size, fmap_sets, fmap_per_iteration):
self.wr_done = False
self.filter_size = filter_size
self.fmap_sets = fmap_sets
self.fmap_per_iteration = fmap_per_iteration
self.rd_set = 0
self.fmap_rd_idx = 0
self.iteration = 0
self.wr_set = 0
self.fmap_wr_idx = 0
self.wr_done = False
def tick(self):
num_iteration = self.filter_size[0] * self.filter_size[1]
if not self.wr_done:
# Write to GLB
if self.dram_wr_chn.valid():
data = self.dram_wr_chn.pop()
self.raw_stats['wr'] += 1
# print "psum_glb wr"
# Write ifmap to glb
# print "ifmap_to_glb: ", in_sets, self.fmap_idx, self.curr_set
addr = self.fmap_sets * self.fmap_wr_idx + self.wr_set
self.wr_set += 1
self.sram.request(WR, addr, data, port=0)
if self.wr_set == self.fmap_sets:
self.wr_set = 0
self.fmap_wr_idx += 1
if self.fmap_wr_idx == self.fmap_per_iteration:
# Done initializing ifmaps and psums
# self.sram.dump()
self.fmap_wr_idx = 0
self.wr_done = True
else:
# Read from GLB and deal with SRAM latency
# print self.rd_chn.vacancy(1), self.rd_chn.rd_ptr.rd(), self.rd_chn.wr_ptr.rd()
if self.rd_chn.vacancy(1) and self.iteration < num_iteration:
addr = self.fmap_sets * self.fmap_rd_idx + self.rd_set
# print "psum req glb", self.iteration, self.fmap_rd_idx, self.rd_set
self.sram.request(RD, addr, port=0)
self.last_read.push(False)
self.rd_set += 1
if self.rd_set == self.fmap_sets:
self.rd_set = 0
self.fmap_rd_idx += 1
if self.fmap_rd_idx == self.fmap_per_iteration:
self.fmap_rd_idx = 0
self.iteration += 1
# Process the last read sent to the GLB SRAM
if self.last_read.valid():
is_zero = self.last_read.pop()
data = [0]*self.chn_per_word if is_zero else \
[e for e in self.sram.response()]
self.rd_chn.push(data)
self.raw_stats['rd'] += 1
# print "psum rd glb", data
# If we can pull an elemnt off of the write channel, do it
# and write it into the location specificed by the current
# fmap_Sets, fmap_wr_idx, and wr_set!
if self.noc_wr_chn.valid():
# print "psum_to_glb: ", self.fmap_wr_idx, self.wr_set
data = self.noc_wr_chn.pop()
self.raw_stats['wr'] += 1
addr = self.fmap_sets * self.fmap_wr_idx + self.wr_set
# print "psum wr glb", self.fmap_wr_idx, self.wr_set, data
self.wr_set += 1
self.sram.request(WR, addr, data, port=1)
if self.wr_set == self.fmap_sets:
self.wr_set = 0
self.fmap_wr_idx += 1
if self.fmap_wr_idx == self.fmap_per_iteration:
# Done initializing ifmaps and psums
# self.sram.dump()
self.fmap_wr_idx = 0
def instantiate(self, arr_x, arr_y,
input_chn, output_chn,
chn_per_word,
ifmap_glb_depth):
# PE static configuration (immutable)
self.name = 'chip'
self.arr_x = arr_x
self.arr_y = arr_y
self.chn_per_word = chn_per_word
self.post_tr_x = arr_x # num output channels = 8
self.post_tr_y = 4 # num tiles = 4
self.pre_tr_ifmap_x = arr_y # num input channels = 4
self.pre_tr_ifmap_y = 4 # num tiles = 4
self.pre_tr_weights_x = arr_y # num input channels = 4
self.pre_tr_weights_y = arr_x # num output channels = 8
self.stat_type = 'show'
# Instantiate DRAM IO channels
self.input_chn = input_chn
self.output_chn = output_chn
# Instantiate input deserializer and output serializer
self.ifmap_wr_chn = Channel()
self.weights_wr_chn = Channel()
self.bias_wr_chn = Channel()
self.deserializer = InputDeserializer(self.input_chn, self.ifmap_wr_chn,
self.weights_wr_chn, self.bias_wr_chn, arr_x, arr_y,
chn_per_word)
self.psum_output_chn = Channel()
self.serializer = OutputSerializer(self.output_chn, self.psum_output_chn)
# Instantiate GLB and GLB channels
self.ifmap_glb_wr_chn = Channel(3)
self.ifmap_rd_chn = Channel(3)
self.ifmap_glb = IFMapGLB(self.ifmap_glb_wr_chn, self.ifmap_rd_chn,
ifmap_glb_depth, chn_per_word)
self.psum_rd_chn = Channel(3)
self.psum_noc_wr_chn = Channel()
# self.psum_glb = PSumGLB(self.psum_wr_chn, self.psum_noc_wr_chn, self.psum_rd_chn,
# psum_glb_depth, chn_per_word)
self.weights_glb_wr_chn = Channel(3)
self.weights_rd_chn = Channel()
self.weights_glb = WeightsGLB(self.weights_glb_wr_chn, self.weights_rd_chn)
self.bias_rd_chn = Channel()
self.bias_glb = BiasGLB(self.bias_wr_chn, self.bias_rd_chn)
# PE Array and local channel declaration
self.pe_array = ModuleList()
self.pe_ifmap_chns = ModuleList()
self.pe_filter_chns = ModuleList()
self.pe_psum_chns = ModuleList()
self.pe_psum_chns.append(ModuleList())
for x in range(self.arr_x):
self.pe_psum_chns[0].append(Channel(32))
# Actual PE array instantiation
for y in range(self.arr_y):
self.pe_array.append(ModuleList())
self.pe_ifmap_chns.append(ModuleList())
self.pe_filter_chns.append(ModuleList())
self.pe_psum_chns.append(ModuleList())
for x in range(self.arr_x):
self.pe_ifmap_chns[y].append(Channel(32))
self.pe_filter_chns[y].append(Channel(32))
self.pe_psum_chns[y+1].append(Channel(32))
self.pe_array[y].append(
PE(x, y,
self.pe_ifmap_chns[y][x],
self.pe_filter_chns[y][x],
self.pe_psum_chns[y][x],
self.pe_psum_chns[y+1][x]
)
)
# Pre Transform IFMap array and local channel declaration
self.pre_tr_ifmap_array = ModuleList()
self.pre_tr_ifmap_in_chns = ModuleList()
self.pre_tr_ifmap_out_chns = ModuleList()
# Actual pre transform IFMap array instantiation
for y in range(self.pre_tr_ifmap_y):
self.pre_tr_ifmap_array.append(ModuleList())
self.pre_tr_ifmap_in_chns.append(ModuleList())
self.pre_tr_ifmap_out_chns.append(ModuleList())
for x in range(self.pre_tr_ifmap_x):
self.pre_tr_ifmap_in_chns[y].append(Channel(32))
self.pre_tr_ifmap_out_chns[y].append(Channel(32))
self.pre_tr_ifmap_array[y].append(
PreTransformIFMap(x, y,
self.pre_tr_ifmap_in_chns[y][x],
self.pre_tr_ifmap_out_chns[y][x]
)
)
# Pre Transform Weight array and local channel declaration
self.pre_tr_weights_array = ModuleList()
self.pre_tr_weights_in_chns = ModuleList()
self.pre_tr_weights_out_chns = ModuleList()
# Actual pre transform Weight array instantiation
for y in range(self.pre_tr_weights_y):
self.pre_tr_weights_array.append(ModuleList())
self.pre_tr_weights_in_chns.append(ModuleList())
self.pre_tr_weights_out_chns.append(ModuleList())
for x in range(self.pre_tr_weights_x):
self.pre_tr_weights_in_chns[y].append(Channel(32))
self.pre_tr_weights_out_chns[y].append(Channel(32))
self.pre_tr_weights_array[y].append(
PreTransformWeights(x, y,
self.pre_tr_weights_in_chns[y][x],
self.pre_tr_weights_out_chns[y][x]
)
)
# Post Transform Array and local channel declaration
self.post_tr_array = ModuleList()
self.post_tr_bias_chns = ModuleList()
self.post_tr_ofmap_in_chns = ModuleList()
self.post_tr_ofmap_out_chns = ModuleList()
# Actual post transform array instantiation
for y in range(self.post_tr_y):
self.post_tr_array.append(ModuleList())
self.post_tr_bias_chns.append(ModuleList())
self.post_tr_ofmap_in_chns.append(ModuleList())
self.post_tr_ofmap_out_chns.append(ModuleList())
for x in range(self.post_tr_x):
self.post_tr_bias_chns[y].append(Channel(32))
self.post_tr_ofmap_in_chns[y].append(Channel(32))
self.post_tr_ofmap_out_chns[y].append(Channel(32))
self.post_tr_array[y].append(
PostTransform(x, y,
self.post_tr_bias_chns[y][x],
self.post_tr_ofmap_in_chns[y][x],
self.post_tr_ofmap_out_chns[y][x]
)
)
# Setup NoC to deliver weights, ifmaps and psums
self.filter_noc = WeightsNoC(self.weights_rd_chn, self.pe_filter_chns, self.chn_per_word)
self.ifmap_noc = IFMapNoC(self.ifmap_rd_chn, self.pe_ifmap_chns, self.arr_x, self.chn_per_word)
self.psum_rd_noc = PSumRdNoC(self.pe_psum_chns[0], self.chn_per_word)
#self.psum_wr_noc = PSumWrNoC(self.pe_psum_chns[-1], self.psum_output_chn, self.chn_per_word)
self.bias_noc = BiasNoC(self.bias_rd_chn, self.post_tr_bias_chns, self.chn_per_word)
# Setup NoC for post transform blocks
self.post_tr_wr_noc = PostTrWrNoC(self.pe_psum_chns[-1], self.post_tr_ofmap_in_chns, self.chn_per_word)
self.post_tr_rd_noc = PostTrRdNoC(self.post_tr_ofmap_out_chns, self.psum_output_chn, self.chn_per_word)
# Instantiate tiler for ifmaps
self.ifmap_tiler = IFMapTiler(self.ifmap_wr_chn, self.pre_tr_ifmap_in_chns, self.chn_per_word)
# Setup NoC for pre transform blocks
#self.pre_tr_ifmap_wr_noc = PreTrIFMapWrNoC(self.ifmap_wr_chn, self.pre_tr_ifmap_in_chns, self.chn_per_word)
self.pre_tr_ifmap_rd_noc = PreTrIFMapRdNoC(self.pre_tr_ifmap_out_chns, self.ifmap_glb_wr_chn, self.chn_per_word)
self.pre_tr_weights_wr_noc = PreTrWeightsWrNoC(self.weights_wr_chn, self.pre_tr_weights_in_chns, self.chn_per_word)
self.pre_tr_weights_rd_noc = PreTrWeightsRdNoC(self.pre_tr_weights_out_chns, self.weights_glb_wr_chn, self.chn_per_word)
class IFMapGLB(Module):
def instantiate(self, wr_chn, rd_chn, glb_depth, chn_per_word):
self.wr_chn = wr_chn
self.rd_chn = rd_chn
self.chn_per_word = chn_per_word
self.name = 'ifmap_glb'
self.stat_type = 'show'
self.raw_stats = {'size' : (glb_depth, chn_per_word), 'rd': 0, 'wr': 0}
self.sram = SRAM(glb_depth, chn_per_word, name=self.name)
self.last_read = Channel(3, name='last_read')
self.image_size = (0, 0)
self.filter_size = (0, 0)
self.fmap_sets = 0
self.full_fmap_sets = 0
self.fmap_per_iteration = 0
self.curr_set = 0
self.fmap_idx = 0
self.iteration = 0
self.tile_in = 0
self.tile_out = 0
self.wr_done = False
self.task_done = True
def configure(self, image_size, filter_size, fmap_sets, full_fmap_sets, tiles_out, fmap_per_iteration):
self.wr_done = False
self.curr_set = 0
self.fmap_idx = 0
self.iteration = 0
self.image_size = image_size
self.filter_size = filter_size
self.fmap_sets = fmap_sets
self.full_fmap_sets = full_fmap_sets
self.fmap_per_iteration = fmap_per_iteration
self.tiles_out = tiles_out
self.tile_in = 0
self.tile_out = 0
self.task_done = False
def tick(self):
num_iteration = self.filter_size[0]*self.filter_size[1]
offset_x = (self.filter_size[0] - 1)//2
offset_y = (self.filter_size[1] - 1)//2
filter_x = self.iteration % self.filter_size[0] - offset_x
filter_y = self.iteration // self.filter_size[0] - offset_y
tiles_in = self.full_fmap_sets // self.fmap_sets
if self.task_done:
return
if not self.wr_done:
# Write to GLB
if self.wr_chn.valid():
data = self.wr_chn.pop()
# print "ifmap_glb wr"
# Write ifmap to glb
# print "ifmap_to_glb: ", in_sets, self.fmap_idx, self.curr_set
addr = self.full_fmap_sets*self.fmap_idx + self.curr_set
self.curr_set += 1
self.sram.request(WR, addr, data)
self.raw_stats['wr'] += len(data)
if self.curr_set == self.full_fmap_sets:
self.curr_set = 0
self.fmap_idx += 1
if self.fmap_idx == self.fmap_per_iteration:
# Done initializing ifmaps and psums
# self.sram.dump()
self.fmap_idx = 0
self.wr_done = True
else:
did_read = False
# Read from GLB and deal with SRAM latency
if self.rd_chn.vacancy(1) and self.iteration < num_iteration and self.tile_in < tiles_in:
fmap_x = self.fmap_idx % self.image_size[0]
fmap_y = self.fmap_idx // self.image_size[0]
ifmap_x, ifmap_y = (fmap_x + filter_x, fmap_y + filter_y)
if (ifmap_x < 0) or (ifmap_x >= self.image_size[0]) or \
(ifmap_y < 0) or (ifmap_y >= self.image_size[1]):
# print "ifmap req zero", self.iteration, self.fmap_idx
self.last_read.push(True)
else:
fmap_idx = (ifmap_y*self.image_size[0]) + ifmap_x
addr = self.fmap_sets*(fmap_idx*tiles_in+self.tile_in) + self.curr_set
# print "ifmap req glb", self.iteration, self.fmap_idx
self.sram.request(RD, addr)
self.raw_stats['rd'] += self.chn_per_word
self.last_read.push(False)
did_read = True
self.curr_set += 1
if self.curr_set == self.fmap_sets:
self.curr_set = 0
self.fmap_idx += 1
if self.fmap_idx == self.fmap_per_iteration:
self.fmap_idx = 0
self.iteration += 1
# Process the last read sent to the GLB SRAM
if self.last_read.valid():
is_zero = self.last_read.pop()
data = [0]*self.chn_per_word if is_zero else \
[e for e in self.sram.response()]
# print "ifmap rd glb", data
self.rd_chn.push(data)
elif not did_read:
if self.iteration == num_iteration:
self.iteration = 0
self.tile_in += 1
if self.tile_in == tiles_in:
self.tile_in = 0
self.tile_out += 1
if self.tile_out == self.tiles_out:
self.tile_out = 0
self.task_done = True