Neuromorphic computing with multi-memristive synapses

Abstract

Neuromorphic computing has emerged as a promising avenue towards building the next generation of intelligent computing systems. It has been proposed that memristive devices, which exhibit history-dependent conductivity modulation, could efficiently represent the synaptic weights in artificial neural networks. However, precise modulation of the device conductance over a wide dynamic range, necessary to maintain high network accuracy, is proving to be challenging. To address this, we present a multi-memristive synaptic architecture with an efficient global counter-based arbitration scheme. We focus on phase change memory devices, develop a comprehensive model and demonstrate via simulations the effectiveness of the concept for both spiking and non-spiking neural networks. Moreover, we present experimental results involving over a million phase change memory devices for unsupervised learning of temporal correlations using a spiking neural network. The work presents a significant step towards the realization of large-scale and energy-efficient neuromorphic computing systems.

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

“…[3,[27][28][29][30] In the hardware implementation of FC networks, e.g., multi-layer perceptrons (MLPs), the weight matrices could be directly mapped to the conductance matrices of memristive crossbar arrays. [3,[27][28][29][30] In the hardware implementation of FC networks, e.g., multi-layer perceptrons (MLPs), the weight matrices could be directly mapped to the conductance matrices of memristive crossbar arrays.…”

Reconfigurable Logic‐in‐Memory and Multilingual Artificial Synapses Based on 2D Heterostructures

“…Computing‐in‐memory architecture is considered as one of the most promising beyond traditional complementary metal‐oxide‐semiconductor (CMOS) technology. The conventional von Neumann computing architecture faces a great challenge to provide higher performance and lower power consumption due to the bottleneck between the logic and memory blocks . As a result, in‐memory computing devices such as artificial synaptic devices and ternary content‐addressable memory (TCAM) based on nonvolatile devices are promising candidates for future electronics and have drawn great research attention recently .…”

“…The conventional von Neumann computing architecture faces a great challenge to provide higher performance and lower power consumption due to the bottleneck between the logic and memory blocks . As a result, in‐memory computing devices such as artificial synaptic devices and ternary content‐addressable memory (TCAM) based on nonvolatile devices are promising candidates for future electronics and have drawn great research attention recently . So far, many kinds of nonvolatile devices have been employed in experimental demonstrations, including resistive random access memory (RRAM), phase‐change memory (PCM), ferroelectric field‐effect transistor (FeFET), and magnetic random access memory (MRAM) .…”

Ionotronic Neuromorphic Devices for Bionic Neural Network Applications

“…These artificial neuromorphic devices have successfully mimicked body senses and neuron functionalities with or without the help of other electronic devices such as pressure sensor and oscilloscope . Recently, bionic neural networks based on artificial neuromorphic devices has been reported . Most of these neuromorphic devices are based on ionic‐electronic coupled devices, i.e., ionotronic devices.…”