def __init__(self, width_i, shape):
self.input_a = MatrixStream(width=width_i, shape=shape, direction='sink', name='input_a')
self.input_b = MatrixStream(width=width_i, shape=shape, direction='sink', name='input_b')
self.input_w = self.input_a.dataport.width
self.n_inputs = self.input_a.dataport.n_elements
self.output_w = calculate_output_width(self.input_w, self.n_inputs)
self.output = DataStream(self.output_w, direction='source', name='output')
self.shape = self.input_a.dataport.shape
def __init__(self, width, shape):
self.width = width
self.shape = shape
self.input = MatrixStream(width=width,
shape=shape,
direction='sink',
name='input')
self.output = MatrixStream(width=width,
shape=shape,
direction='source',
name='output')
def __init__(self, input_w, row_length, N, invert=False):
self.row_length = row_length
self.invert = invert
self.input = DataStream(width=input_w, direction='sink', name='input')
self.output = MatrixStream(width=input_w,
shape=(N, ),
direction='source',
name='output')
self.input_w = len(self.input.data)
self.output_w = self.output.dataport.width
self.shape = self.output.dataport.shape
self.N = self.output.dataport.shape[0]
def __init__(self, data_w, N, invert=False):
self.invert = invert
self.input = MatrixStream(width=data_w,
shape=(N, ),
direction='sink',
name='input')
self.output = MatrixStream(width=data_w,
shape=(N, N),
direction='source',
name='output')
self.data_w = self.input.dataport.width
self.shape_i = self.input.dataport.shape
self.shape_o = self.output.dataport.shape
self.N = self.output.dataport.shape[0]
class SubmatrixRegisters(Elaboratable):
def __init__(self, data_w, N, invert=False):
self.invert = invert
self.input = MatrixStream(width=data_w,
shape=(N, ),
direction='sink',
name='input')
self.output = MatrixStream(width=data_w,
shape=(N, N),
direction='source',
name='output')
self.data_w = self.input.dataport.width
self.shape_i = self.input.dataport.shape
self.shape_o = self.output.dataport.shape
self.N = self.output.dataport.shape[0]
def get_ports(self):
ports = [self.input[f] for f in self.input.fields]
ports += [self.output[f] for f in self.output.fields]
return ports
def elaborate(self, platform):
m = Module()
sync = m.d.sync
comb = m.d.comb
if self.invert:
_col = lambda col: col
else:
_col = lambda col: self.N - 1 - col
with m.If(self.input.accepted()):
for row in range(self.N): # row iteration
sync += self.output.dataport.matrix[row, _col(0)].eq(
self.input.dataport.matrix[row]
) # append column from input
for col in range(
1, self.N): # shift to the right the other columns
sync += self.output.dataport.matrix[row, _col(col)].eq(
self.output.dataport.matrix[row, _col(col - 1)])
with m.If(self.input.accepted()):
sync += self.output.valid.eq(1)
with m.Elif(self.output.accepted()):
sync += self.output.valid.eq(0)
comb += self.input.ready.eq(self.output.accepted()
| ~self.output.valid)
return m
def __init__(self, data_w, input_shape, N, invert=False):
assert input_shape[0] % N == 0, (
f'image height must be a multiple of N. Psss, you can use Padder() to append zeros!'
)
assert input_shape[1] % N == 0, (
f'image width must be a multiple of N. Psss, you can use Padder() to append zeros!'
)
self.input = DataStream(width=data_w, direction='sink', name='input')
self.output = MatrixStream(width=data_w,
shape=(N, N),
direction='source',
name='output')
self.matrix_feeder = MatrixFeeder(data_w,
input_shape,
N,
invert=invert)
self.output_shape = (int(input_shape[0] / N), int(input_shape[1] / N))
self.N = N
def __init__(self, width, shape, n_cores):
self.cores = [DotProduct(width, shape) for _ in range(n_cores)]
self.input_a = MatrixStream(width=width,
shape=shape,
direction='sink',
name='input_a')
self.input_b = MatrixStream(width=width,
shape=shape,
direction='sink',
name='input_b')
self.output_w = self.cores[0].output_w
self.output = DataStream(self.output_w,
direction='source',
name='output')
self.input_w = self.input_a.dataport.width
self.n_inputs = self.input_a.dataport.n_elements
self.shape = self.input_a.dataport.shape
self.n_cores = len(self.cores)
def __init__(self, data_w, input_shape, N, invert=False):
self.input_shape = input_shape
self.output_shape = (input_shape[0] + 1 - N, input_shape[1] + 1 - N)
self.invert = invert
self.input = DataStream(width=data_w, direction='sink', name='input')
self.output = MatrixStream(width=data_w,
shape=(N, N),
direction='source',
name='output')
self.data_w = len(self.input.data)
self.shape = self.output.dataport.shape
self.N = self.output.dataport.shape[0]
def __init__(self, width, input_shape, N, n_cores):
self.input_shape = input_shape
self.n_cores = n_cores
self.matrix_feeder = MatrixFeeder(data_w=width,
input_shape=input_shape,
N=N,
invert=False)
self.farm = Farm(width=width,
shape=(N, N),
n_cores=n_cores)
self.coeff = MatrixStream(width=width, shape=(N, N), direction='sink', name='coeff')
self.input = DataStream(width=width, direction='sink', name='input')
self.output = DataStream(width=len(self.farm.output.data), direction='source', name='output')
self.input_w = len(self.input.data)
self.output_w = len(self.output.data)
self.shape = self.coeff.dataport.shape
self.N = self.coeff.dataport.shape[0]
def TreeHighestUnsignedWrapped(width_i, n_stages, reg_in, reg_out):
core = TreeHighestUnsigned(width_i=width_i,
n_stages=n_stages,
reg_in=reg_in,
reg_out=reg_out)
latency = core.latency
n_inputs = len(core.inputs)
input_stream = MatrixStream(width_i,
shape=(n_inputs, ),
direction='sink',
name='input')
output_stream = DataStream(core.output.width,
direction='source',
name='output')
input_map = {}
for i in range(n_inputs):
input_map['data_' + str(i)] = core.inputs[i].name
return StreamWrapper(wrapped_core=core,
input_stream=input_stream,
output_stream=output_stream,
input_map=input_map,
output_map={'data': 'output'},
latency=latency)
class MatrixFeederSkip(MatrixFeeder):
def __init__(self, data_w, input_shape, N, invert=False):
assert input_shape[0] % N == 0, (
f'image height must be a multiple of N. Psss, you can use Padder() to append zeros!'
)
assert input_shape[1] % N == 0, (
f'image width must be a multiple of N. Psss, you can use Padder() to append zeros!'
)
self.input = DataStream(width=data_w, direction='sink', name='input')
self.output = MatrixStream(width=data_w,
shape=(N, N),
direction='source',
name='output')
self.matrix_feeder = MatrixFeeder(data_w,
input_shape,
N,
invert=invert)
self.output_shape = (int(input_shape[0] / N), int(input_shape[1] / N))
self.N = N
def get_ports(self):
ports = [self.input[f] for f in self.input.fields]
ports += [self.output[f] for f in self.output.fields]
return ports
def elaborate(self, platform):
m = Module()
sync = m.d.sync
comb = m.d.comb
pooling_counter_row = Signal(range(self.N))
pooling_counter_col = Signal(range(self.N))
m.submodules.matrix_feeder = matrix_feeder = self.matrix_feeder
row, col = img_position_counter(m, sync, self.output,
self.output_shape)
feeder_row, feeder_col = img_position_counter(
m, sync, matrix_feeder.output, matrix_feeder.output_shape)
# input --> matrix_feeder
comb += [
matrix_feeder.input.valid.eq(self.input.valid),
matrix_feeder.input.last.eq(self.input.last),
matrix_feeder.input.data.eq(self.input.data),
self.input.ready.eq(matrix_feeder.input.ready),
]
comb += self.output.dataport.eq(matrix_feeder.output.dataport)
comb += self.output.last.eq(is_last(row, col, self.output_shape))
with m.If(matrix_feeder.output.accepted()):
sync += pooling_counter_row.eq(_incr(pooling_counter_row, self.N))
with m.If(feeder_row == matrix_feeder.output_shape[1] - 1):
sync += pooling_counter_row.eq(0)
sync += pooling_counter_col.eq(
_incr(pooling_counter_col, self.N))
with m.If(matrix_feeder.output.last):
sync += [
pooling_counter_row.eq(0),
pooling_counter_col.eq(0),
]
with m.FSM() as fsm:
with m.State("normal"):
with m.If((pooling_counter_row == 0)
& (pooling_counter_col == 0)):
comb += [
self.output.valid.eq(matrix_feeder.output.valid),
matrix_feeder.output.ready.eq(self.output.ready),
]
with m.Else():
comb += [
self.output.valid.eq(0),
matrix_feeder.output.ready.eq(1),
]
with m.If(self.output.accepted() & self.output.last):
m.next = "last"
with m.State("last"):
comb += [
self.output.valid.eq(0),
matrix_feeder.output.ready.eq(1),
]
with m.If(self.input.accepted() & self.input.last):
m.next = "normal"
return m
class Farm(Elaboratable):
_doc_ = """
"Farm" of DotProduct cores, for parallel computation.
The performed operation is the dot product of two NxM
matrixes.
Keep in mind that since throughput will never be higher
than one output per clock, it doesn't make sense to use
a higher number of DotProduct cores than the latency of
each one of them.
The dataflow is controlled ONLY by the input_a Stream interface.
The input_b stream interface is DUMMY, and should always
have valid values in the input. The ready of the input_b
interface will be attached to input_a.accepted(), and a valid=1
will be assumed. Why?
I want to avoid a combinational path between the valid of input_b
and the ready of input_a.
Interfaces
----------
input_a : Matrix Stream, input
Input a matrix data.
input_b : Matrix Stream, input
Input b matrix data.
TO DO: should not be a stream, but plain "matrix shaped" values.
output : Data Stream, output
Dot product computated value.
Parameters
----------
width : int
Bit width of both inputs.
shape : tuple
Input shape (N, M).
n_cores : int
Number of paralell computations of dot product.
"""
def __init__(self, width, shape, n_cores):
self.cores = [DotProduct(width, shape) for _ in range(n_cores)]
self.input_a = MatrixStream(width=width,
shape=shape,
direction='sink',
name='input_a')
self.input_b = MatrixStream(width=width,
shape=shape,
direction='sink',
name='input_b')
self.output_w = self.cores[0].output_w
self.output = DataStream(self.output_w,
direction='source',
name='output')
self.input_w = self.input_a.dataport.width
self.n_inputs = self.input_a.dataport.n_elements
self.shape = self.input_a.dataport.shape
self.n_cores = len(self.cores)
def get_ports(self):
ports = []
ports += [self.input_a[f] for f in self.input_a.fields]
ports += [self.input_b[f] for f in self.input_b.fields]
ports += [self.output[f] for f in self.output.fields]
return ports
def elaborate(self, platform):
m = Module()
sync = m.d.sync
comb = m.d.comb
current_core_sink = Signal(range(self.n_cores))
current_core_source = Signal(range(self.n_cores))
# DUMMY input_b interface
# comb += [self.input_b.ready.eq(self.input_a.accepted())]
for i, core in enumerate(self.cores):
m.submodules['core_' + str(i)] = core
comb += core.input_b.dataport.eq(
self.input_b.dataport) # same coefficients for everybody
with m.If(current_core_sink == i):
comb += [
self.input_a.ready.eq(core.input_a.ready),
self.input_b.ready.eq(core.input_b.ready),
]
comb += [
core.input_a.valid.eq(self.input_a.valid),
core.input_b.valid.eq(self.input_b.valid),
core.input_a.dataport.eq(self.input_a.dataport),
]
with m.Else():
comb += [
core.input_a.valid.eq(0),
core.input_b.valid.eq(0),
core.input_a.dataport.eq_const(0),
]
with m.If(current_core_source == i):
comb += [
self.output.valid.eq(core.output.valid),
self.output.data.eq(core.output.data),
]
comb += [
core.output.ready.eq(self.output.ready),
]
with m.Else():
comb += [
core.output.ready.eq(0),
]
with m.If(self.input_a.accepted()):
sync += current_core_sink.eq(_incr(current_core_sink,
self.n_cores))
with m.If(self.output.accepted()):
sync += current_core_source.eq(
_incr(current_core_source, self.n_cores))
return m
class DotProduct(Elaboratable):
#
# WARNING:
# The dataflow is controlled ONLY by the input_a AXIS interface.
# The input_b AXIS interface is DUMMY, and should always have valid values in the input.
# The ready of the input_b interface will be attached to input_a.accepted(), and a valid=1
# will be assumed.
#
# Why?
# I want to avoid a combinational path between the valid of input_b and the ready of input_a.
#
def __init__(self, width_i, shape):
self.input_a = MatrixStream(width=width_i, shape=shape, direction='sink', name='input_a')
self.input_b = MatrixStream(width=width_i, shape=shape, direction='sink', name='input_b')
self.input_w = self.input_a.dataport.width
self.n_inputs = self.input_a.dataport.n_elements
self.output_w = calculate_output_width(self.input_w, self.n_inputs)
self.output = DataStream(self.output_w, direction='source', name='output')
self.shape = self.input_a.dataport.shape
def get_ports(self):
ports = []
ports += [self.input_a[f] for f in self.input_a.fields]
ports += [self.input_b[f] for f in self.input_b.fields]
ports += [self.output[f] for f in self.output.fields]
return ports
def elaborate(self, platform):
m = Module()
sync = m.d.sync
comb = m.d.comb
tmp_input_a = Signal(self.input_w * self.n_inputs)
tmp_input_b = Signal(self.input_w * self.n_inputs)
counter = Signal(range(self.n_inputs))
m.submodules['mac'] = mac = MAC(input_w=self.input_w, output_w=self.output_w)
comb += [mac.input_a.eq(tmp_input_a[0:self.input_w]),
mac.input_b.eq(tmp_input_b[0:self.input_w]),]
# DUMMY input_b interface
comb += [self.input_b.ready.eq(self.input_a.accepted())]
with m.FSM() as fsm:
with m.State("IDLE"):
comb += [self.input_a.ready.eq(self.output.accepted() | ~self.output.valid),
mac.clr.eq(1),
mac.clken.eq(0),]
with m.If(self.input_a.accepted()):
m.next = "BUSY"
sync += [tmp_input_a.eq(Cat(*self.input_a.flat)), #self.input_a.data),
tmp_input_b.eq(Cat(*self.input_b.flat)), #Cat(*self.input_b)),
counter.eq(0),]
with m.If(self.output.accepted()):
sync += self.output.valid.eq(0)
with m.State("BUSY"):
comb += [self.input_a.ready.eq(0),
mac.clr.eq(0),
mac.clken.eq(1),]
sync += [tmp_input_b.eq(tmp_input_b >> self.input_w),
tmp_input_a.eq(tmp_input_a >> self.input_w),]
with m.If(mac.valid_o):
sync += counter.eq(counter + 1)
with m.If(counter == self.n_inputs - 1):
m.next = "IDLE"
sync += [self.output.data.eq(mac.output),
self.output.valid.eq(1),]
return m
class RowFifos(Elaboratable):
""" N fifos that work synchronized to provide Nx1 (N=row)
vector of data.
"""
def __init__(self, input_w, row_length, N, invert=False):
self.row_length = row_length
self.invert = invert
self.input = DataStream(width=input_w, direction='sink', name='input')
self.output = MatrixStream(width=input_w,
shape=(N, ),
direction='source',
name='output')
self.input_w = len(self.input.data)
self.output_w = self.output.dataport.width
self.shape = self.output.dataport.shape
self.N = self.output.dataport.shape[0]
def get_ports(self):
ports = [self.input[f] for f in self.input.fields]
ports += [self.output[f] for f in self.output.fields]
return ports
def elaborate(self, platform):
m = Module()
sync = m.d.sync
comb = m.d.comb
fifo = [
SyncFIFOBuffered(width=self.input_w, depth=self.row_length + 4)
for _ in range(self.N)
]
fifo_r_rdy = [Signal() for _ in range(self.N)]
fifo_r_valid = [Signal() for _ in range(self.N)]
w_en = [Signal() for _ in range(self.N - 1)]
for n in range(self.N):
m.submodules['fifo_' + str(n)] = fifo[n]
comb += [
fifo_r_rdy[n].eq((fifo[n].level < self.row_length)
| self.output.accepted()),
]
# first fifo
comb += [
self.input.ready.eq(fifo[0].w_rdy),
fifo[0].w_en.eq(self.input.accepted()),
fifo[0].w_data.eq(self.input.data),
]
for n in range(self.N - 1):
comb += [
fifo_r_valid[n].eq((fifo[n + 1].level == self.row_length)
& (fifo[n].r_rdy)),
fifo[n].r_en.eq((self.output.accepted() | ~fifo_r_valid[n])),
fifo[n + 1].w_en.eq(fifo[n].r_rdy & fifo[n].r_en),
fifo[n + 1].w_data.eq(fifo[n].r_data),
]
# last fifo
n = self.N - 1
comb += [
fifo_r_valid[n].eq(fifo[n].r_rdy),
fifo[n].r_en.eq(self.output.accepted()),
]
# output
comb += [
self.output.valid.eq(_and(fifo_r_valid)),
]
for n in range(self.N):
if self.invert:
comb += self.output.dataport.matrix[n].eq(fifo[n].r_data)
else:
comb += self.output.dataport.matrix[n].eq(fifo[self.N - 1 -
n].r_data)
return m