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 test_main_wrapper(latency):
core = StreamWrapper(wrapped_core=ExampleCore(16, latency),
input_stream=DataStream(16, direction='sink', name='input'),
output_stream=DataStream(16, direction='source', name='output'),
input_map={'data': 'data_i'},
output_map={'data': 'data_o'},
latency=latency)
ports = core.get_ports()
run(core, 'cnn.tests.test_stream_wrapper', ports=ports, vcd_file=f'./test_stream_wrapper.vcd')
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, width_i, width_c, width_acc=None, shift=None):
if width_acc is None:
width_acc = 48
if shift is None:
shift = 0
output_w = width_acc - shift
self.shift = shift
self.accumulator = Signal(signed(width_acc))
self.input = DataStream(width=width_i, direction='sink', name='input')
self.output = DataStream(width=output_w,
direction='source',
name='output')
self.r_data = Signal(signed(width_c))
self.r_en = Signal()
self.r_rdy = Signal()
self.latency = 5
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 __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):
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, data_w, input_shape, N, mode):
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!'
)
assert mode in self._modes, 'Unsupported mode'
self.mode = mode
self.matrix_feeder = MatrixFeederSkip(data_w=data_w,
input_shape=input_shape,
N=N,
invert=False)
self.input = DataStream(width=data_w, direction='sink', name='input')
self.output = DataStream(width=data_w,
direction='source',
name='output')
self.N = N
self.output_shape = [int(x / N) for x in input_shape]
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_i, width_w, n_inputs, rom_init):
assert len(rom_init) % (n_inputs + 1) == 0
accum_w = accum_req_bits(width_i, width_w, n_inputs + 1) # +1 bias
shift = width_w - 1 # compensate weights gain
self.n_inputs = n_inputs
self.rom = CircularROM(width=width_w, init=rom_init)
self.macc = StreamMacc(width_i=width_i,
width_c=width_w,
width_acc=accum_w,
shift=shift)
output_w = len(self.macc.output.data)
assert output_w == accum_w - shift, (
f'{output_w} == {accum_w} - {shift}')
self.input = DataStream(width=width_i, direction='sink', name='input')
self.output = DataStream(width=output_w,
direction='source',
name='output')
def __init__(self, data_w, input_shape, output_shape, fill_value=0):
if input_shape[0] == output_shape[0] and input_shape[
1] == output_shape[1]:
# no operation, just last generation
setattr(self, 'elaborate', self.elaborate_nop)
elif input_shape[0] <= output_shape[0] and input_shape[
1] <= output_shape[1]:
setattr(self, 'elaborate', self.elaborate_padder)
elif input_shape[0] >= output_shape[0] and input_shape[
1] >= output_shape[1]:
setattr(self, 'elaborate', self.elaborate_cropper)
else:
raise RuntimeError(
'Output image must be cant be bigger in one dimension and smaller in the other one'
)
self.input_shape = input_shape
self.output_shape = output_shape
self.fill_value = fill_value
self.input = DataStream(width=data_w, direction='sink', name='input')
self.output = DataStream(width=data_w,
direction='source',
name='output')
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
def __init__(self, width, leak=0):
self.leak = leak
self.input = DataStream(width=width, direction='sink', name='input')
self.output = DataStream(width=width,
direction='source',
name='output')
class mlpNode(Elaboratable):
_doc_ = """
MLP Node instantiates a Stream Macc and a Circular ROM
to store the corresponding weights. Both input and output
are Stream interfaces.
This MLP Node can actually do the job of N neurons serially
where each neuron will require (n_inputs + 1) weights stored.
Parameters
----------
width_i : int
Bit width of data in stream interface.
width_w : int
Bit width of data in the ROM.
n_inputs : int
Number of inputs for each neuron.
rom_init : list
List with weights to initialize the ROM. It should
have the form
[N0_W0, N0_W1, ..., N0_Wn-1, N0_Wbias,
N1_W0, N1_W1, ..., N1_Wn-1, N1_Wbias,
...
]
where Nx_Wy refers to the weight of the sample 'y'
of neuron 'x'.
"""
def __init__(self, width_i, width_w, n_inputs, rom_init):
assert len(rom_init) % (n_inputs + 1) == 0
accum_w = accum_req_bits(width_i, width_w, n_inputs + 1) # +1 bias
shift = width_w - 1 # compensate weights gain
self.n_inputs = n_inputs
self.rom = CircularROM(width=width_w, init=rom_init)
self.macc = StreamMacc(width_i=width_i,
width_c=width_w,
width_acc=accum_w,
shift=shift)
output_w = len(self.macc.output.data)
assert output_w == accum_w - shift, (
f'{output_w} == {accum_w} - {shift}')
self.input = DataStream(width=width_i, direction='sink', name='input')
self.output = DataStream(width=output_w,
direction='source',
name='output')
def get_ports(self):
ports = []
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
m.submodules.rom = rom = self.rom
m.submodules.macc = macc = self.macc
cnt = Signal(range(self.n_inputs))
output_data = Signal(signed(len(self.output.data)))
comb += macc.r_data.eq(rom.r_data)
comb += macc.r_rdy.eq(rom.r_rdy)
comb += rom.r_en.eq(macc.r_en)
comb += rom.restart.eq(0) # should be unnecessary if the
# inputs are correct.
nxt_cnt = Signal.like(cnt)
comb += nxt_cnt.eq(_incr(cnt, self.n_inputs))
with m.If(self.input.accepted()):
sync += cnt.eq(nxt_cnt)
with m.FSM() as fsm:
with m.State("INPUT"):
comb += self.input.ready.eq(macc.input.ready)
comb += macc.input.valid.eq(self.input.valid)
comb += macc.input.data.eq(self.input.data)
comb += macc.input.last.eq(0)
with m.If(macc.input.accepted() & (nxt_cnt == 0)):
m.next = "BIAS"
with m.State("BIAS"):
comb += self.input.ready.eq(0)
comb += macc.input.valid.eq(1)
comb += macc.input.data.eq(
1
) # should it be bigger? what granularity should the bias have?
comb += macc.input.last.eq(1)
with m.If(macc.input.accepted()):
m.next = "INPUT"
comb += output_data.eq(macc.output.data)
comb += self.output.valid.eq(macc.output.valid)
comb += self.output.data.eq(output_data)
comb += self.output.last.eq(0) # self.input.last + delay?
comb += macc.output.ready.eq(self.output.ready)
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 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
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 StreamMacc(Elaboratable):
_doc_ = """
Multiplier and accumulator with selectable output division to scale
down result and a Stream interface.
The main input data interface is a Stream interface, while
for the coefficients a memory read port interface is used.
While the memory has r_rdy==1, the dataflow control will be
done in the input data stream interface. If the coefficients
come from the same interface as the input data, just assign
the ports of the Stream Macc you should just:
* ignore r_en
* tell the core that the memory is ready to be read when
the input data is valid by assigning:
core.r_rdy <-- core.input.valid
Interfaces
----------
input : Data Stream, input
Input data.
Each product between the input data and the coeff data
will be accumulated, until a last is asserted. A last
will flush the pipeline and output the valid result,
clearing the accumulator afterwards.
coeff : {r_data, r_en, r_rdy}, input
Input coefficients.
TO DO: Implement ReadportInterface
output : Data Stream, output
Output data. Will output valid data after a last is
asserted in the input interface. Otherwise, it will
keep accumulating.
Parameters
----------
width_i : int
Bit width of data in stream interface.
width_c : int
Bit width of the coefficients.
width_acc : int
Bit width of the accumulator.
shift : int
The accumulator result will be shifted to the right by
this number, so the output will be (accumulator / 2**shift).
"""
def __init__(self, width_i, width_c, width_acc=None, shift=None):
if width_acc is None:
width_acc = 48
if shift is None:
shift = 0
output_w = width_acc - shift
self.shift = shift
self.accumulator = Signal(signed(width_acc))
self.input = DataStream(width=width_i, direction='sink', name='input')
self.output = DataStream(width=output_w,
direction='source',
name='output')
self.r_data = Signal(signed(width_c))
self.r_en = Signal()
self.r_rdy = Signal()
self.latency = 5
def get_ports(self):
ports = [self.r_data, self.r_en, self.r_rdy]
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()
comb = m.d.comb
sync = m.d.sync
clken = Signal()
accepted_last = self.input.accepted() & self.input.last
last_delayed = signal_delay(m, accepted_last, self.latency, ce=clken)
accum_shifted = Signal(signed(self.output.dataport.width))
with m.FSM() as fsm:
with m.State("ACCUM"):
comb += self.input.ready.eq(self.r_rdy)
comb += self.output.valid.eq(0)
comb += self.r_en.eq(self.input.accepted())
comb += clken.eq(self.input.accepted())
with m.If(self.input.accepted() & self.input.last):
m.next = "LAST"
with m.State("LAST"):
comb += self.input.ready.eq(0)
comb += self.output.valid.eq(last_delayed)
comb += self.r_en.eq(0)
comb += clken.eq(~self.output.valid | self.output.accepted())
with m.If(self.output.accepted()):
m.next = "ACCUM"
_get_input = lambda x: Mux(self.input.accepted(), x, 0)
_get_accum = lambda x: Mux(self.output.accepted(), 0, x)
pipeline = Pipeline()
a0, b0 = pipeline.add_stage([
_get_input(self.input.data.as_signed()),
_get_input(self.r_data.as_signed())
])
a1, b1 = pipeline.add_stage([a0, b0])
m2, = pipeline.add_stage([a1 * b1])
m3, = pipeline.add_stage([m2])
out, = pipeline.add_stage([_get_accum(self.accumulator) + m3])
pipeline.generate(m=m, ce=clken, domain='sync')
comb += self.accumulator.eq(out)
comb += accum_shifted.eq(out[self.shift:].as_signed())
comb += self.output.data.eq(accum_shifted)
comb += self.output.last.eq(self.output.valid)
return m