forked from opendacs/PyHCL
62 lines
1.3 KiB
Python
62 lines
1.3 KiB
Python
from pyhcl import *
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W = 8 # 位宽
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def matrixMul(x: int, y: int, z: int):
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"""
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x*y × y*z 矩阵乘法电路
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"""
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class MatrixMul(Module):
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io = IO(
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a=Input(Vec(x, Vec(y, U.w(W)))),
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b=Input(Vec(y, Vec(z, U.w(W)))),
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o=Output(Vec(x, Vec(z, U.w(W)))),
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)
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for i, a_row in enumerate(io.a):
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for j, b_col in enumerate(zip(*io.b)):
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io.o[i][j] @= Sum(a * b for a, b in zip(a_row, b_col))
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return MatrixMul()
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def bias(n):
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return U.w(W)(n)
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def weight(lst):
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return VecInit(U.w(W)(i) for i in lst)
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def neurons(w, b):
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"""
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参数：权重向量 w，偏移量 b
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输出：神经网络神经元电路 *注：暂无通过非线性传递函数
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"""
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class Unit(Module):
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io = IO(
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i=Input(Vec(len(w), U.w(W))),
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o=Output(U.w(W))
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)
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m = matrixMul(1, len(w), 1).io
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m.a @= io.i
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m.b @= w
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io.o @= m.o[0][0] + b
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return Unit()
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def main():
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# 得到权重向量为[3, 4, 5, 6, 7, 8, 9, 10]，偏移量为14的神经元电路
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n = neurons(weight([3, 4, 5, 6, 7, 8, 9, 10]), bias(14))
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f = Emitter.dump(Emitter.emit(n), "neurons.fir")
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Emitter.dumpVerilog(f)
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if __name__ == '__main__':
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main()
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