A differential memristive synapse circuit for on-line learning in neuromorphic computing systems

TL;DR

This paper introduces a novel differential memristive synapse circuit that decouples currents to enable efficient, spike-based online learning in neuromorphic systems, reducing variability effects and improving throughput.

Contribution

It proposes a new circuit design based on the Gilbert normalizer that supports spike-based learning without overlapping pulses, enhancing neuromorphic learning capabilities.

Findings

Circuit reduces variability effects in memristive devices

Supports spike-based learning without overlapping pulses

Validated with SPICE and behavioral simulations

Abstract

Spike-based learning with memristive devices in neuromorphic computing architectures typically uses learning circuits that require overlapping pulses from pre- and post-synaptic nodes. This imposes severe constraints on the length of the pulses transmitted in the network, and on the network's throughput. Furthermore, most of these circuits do not decouple the currents flowing through memristive devices from the one stimulating the target neuron. This can be a problem when using devices with high conductance values, because of the resulting large currents. In this paper we propose a novel circuit that decouples the current produced by the memristive device from the one used to stimulate the post-synaptic neuron, by using a novel differential scheme based on the Gilbert normalizer circuit. We show how this circuit is useful for reducing the effect of variability in the memristive devices, and how it is ideally suited for spike-based learning mechanisms that do not require overlapping pre- and post-synaptic pulses. We demonstrate the features of the proposed synapse circuit with SPICE simulations, and validate its learning properties with high-level behavioral network simulations which use a stochastic gradient descent learning rule in two classification tasks.