  Adder neural network (AdderNet) is a new kind of deep model that replaces the
original massive multiplications in convolutions by additions while preserving
the high performance. Since the hardware complexity of additions is much lower
than that of multiplications, the overall energy consumption is thus reduced
significantly. To further optimize the hardware overhead of using AdderNet,
this paper studies the winograd algorithm, which is a widely used fast
algorithm for accelerating convolution and saving the computational costs.
Unfortunately, the conventional Winograd algorithm cannot be directly applied
to AdderNets since the distributive law in multiplication is not valid for the
l1-norm. Therefore, we replace the element-wise multiplication in the Winograd
equation by additions and then develop a new set of transform matrixes that can
enhance the representation ability of output features to maintain the
performance. Moreover, we propose the l2-to-l1 training strategy to mitigate
the negative impacts caused by formal inconsistency. Experimental results on
both FPGA and benchmarks show that the new method can further reduce the energy
consumption without affecting the accuracy of the original AdderNet.
