Dendritic-Inspired Processing Enables Bio-Plausible STDP in Compound Binary Synapses

TL;DR

This paper introduces a dendritic-inspired processing architecture that leverages stochastic binary NVM devices to implement biologically-plausible STDP learning in neuromorphic systems, enabling low-power, high-density brain-inspired hardware.

Contribution

It proposes a novel dendritic-inspired architecture and CMOS neuron circuits that turn stochastic binary memory behavior into a feature for STDP learning.

Findings

Enables STDP with binary NVM devices using spike attenuation and delays.

Achieves biologically-plausible learning with practical binary memory devices.

Facilitates low-power, high-density neuromorphic hardware implementations.

Abstract

Brain-inspired learning mechanisms, e.g. spike timing dependent plasticity (STDP), enable agile and fast on-the-fly adaptation capability in a spiking neural network. When incorporating emerging nanoscale resistive non-volatile memory (NVM) devices, with ultra-low power consumption and high-density integration capability, a spiking neural network hardware would result in several orders of magnitude reduction in energy consumption at a very small form factor and potentially herald autonomous learning machines. However, actual memory devices have shown to be intrinsically binary with stochastic switching, and thus impede the realization of ideal STDP with continuous analog values. In this work, a dendritic-inspired processing architecture is proposed in addition to novel CMOS neuron circuits. The utilization of spike attenuations and delays transforms the traditionally undesired stochastic behavior of binary NVMs into a useful leverage that enables biologically-plausible STDP learning. As a result, this work paves a pathway to adopt practical binary emerging NVM devices in brain-inspired neuromorphic computing.