Bio-Inspired Stochastic Computing Using Binary CBRAM Synapses

Citations Over TimeTop 1% of 2013 papers

Abstract

In this paper, we present an alternative approach to neuromorphic systems based on multilevel resistive memory synapses and deterministic learning rules. We demonstrate an original methodology to use conductive-bridge RAM (CBRAM) devices as, easy to program and low-power, binary synapses with stochastic learning rules. New circuit architecture, programming strategy, and probabilistic spike-timing dependent plasticity (STDP) learning rule for two different CBRAM configurations with-selector (1T-1R) and without-selector (1R) are proposed. We show two methods (intrinsic and extrinsic) for implementing probabilistic STDP rules. Fully unsupervised learning with binary synapses is illustrated through two example applications: 1) real-time auditory pattern extraction (inspired from a 64-channel silicon cochlea emulator); and 2) visual pattern extraction (inspired from the processing inside visual cortex). High accuracy (audio pattern sensitivity > 2, video detection rate > 95%) and low synaptic-power dissipation (audio 0.55 μW, video 74.2 μW) are shown. The robustness and impact of synaptic parameter variability on system performance are also analyzed.

Related Papers

• → Recent Progress of Optoelectronic and All‐Optical Neuromorphic Devices: A Comprehensive Review of Device Structures, Materials, and Applications(2020)94 cited
• → CMOS-compatible neuromorphic devices for neuromorphic perception and computing: a review(2023)93 cited
• → Mechanisms and applications of neuromorphic sensors for intelligent visual perception(2023)11 cited
• → A study on supporting spiking neural network models based on multiple neuromorphic processors(2019)2 cited
• → Neuromorphic medical electronics(2002)