Memory-Centric Computing for Image Classification Using SNN with RRAM

Neuromorphic computing, exemplified by spiking neural networks (SNN), seeks to replicate human brain functionality through event-driven processes, encoding information via spikes, and adopting biological learning principles. Its comparative advantage over traditional computing lies in the eventdriven nature of computations, promising notably high energy efficiency. However, the hardware implementation of SNN poses limitations for various applications. This study proposes an In-memory Computing (IMC) approach, utilizing a Resistive RAM-based (RRAM) crossbar array to expedite the SNN algorithm. The investigation scrutinizes the accuracy of three network variants-fp32, fp16, and int8-utilizing different data types. Remarkably, by reducing the datasize to one fourth of the original size, the accuracy increased by 1.17% after retraining. Additionally, quantizing the network from fp32 to 8-bit fixed point, and using an RRAM crossbar array, yielded savings of similar to 1634x in memory access energy, similar to 1636x in memory access latency, and similar to 132x in computations energy. Furthermore, utilizing the RRAM crossbar array for the acceleration of the quantized SNN algorithm yielded similar to 10x reduction in average power consumption per inference, and similar to 159x savings in required area.