An Inference and Learning Engine for Spiking Neural Networks in Computational RAM (CRAM)

Abstract

Spiking Neural Networks (SNN) represent a biologically inspired computation model capable of emulating neural computation in human brain and brain-like structures. The main promise is very low energy consumption. Unfortunately, classic Von Neumann architecture based SNN accelerators often fail to address demanding computation and data transfer requirements efficiently at scale. In this work, we propose a promising alternative, an in-memory SNN accelerator based on Spintronic Computational RAM (CRAM) to overcome scalability limitations, which can reduce the energy consumption by up to 164.1$\times$ when compared to a representative ASIC solution.

References in corpus (5)

• A 0.086-mm${}^2$ 12.7-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28nm CMOS
• Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System
• Hybrid Spintronic-CMOS Spiking Neural Network With On-Chip Learning: Devices, Circuits and Systems
• All-Spin Bayesian Neural Networks
• SpinAPS: A High-Performance Spintronic Accelerator for Probabilistic Spiking Neural Networks

Cited by in corpus (1)

• On Error Correction for Nonvolatile Processing-In-Memory