A Pulse-Width Modulation Neuron With Continuous Activation For Processing-In-Memory Engines

Processing-in-memory engines have successfully been applied to accelerate deep neural networks. For improving computing efficiency, spiking-based designs are widely explored. However, spiking-based designs quantize inter-layer signals naturally, leading to performance loss. In addition, the spike mismatch effect makes digital processing necessary, impeding direct signal transfer between layers and thus resulting in longer latency. In this paper, we propose a novel neuron design based on pulse width modulation, avoiding the quantization step and bypassing spike mismatch via the continuous activation. The computation latency and circuit complexity can significantly be reduced due to the absence of quantization and digital processing steps, while keeping a competitive performance. Simulation results show that the proposed neuron design can achieve > 100x speedup compared with spiking-based designs. The area and power consumption can be reduced up to 74.87% and 25.63%.