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Title: Binary operations on neuromorphic hardware with application to linear algebraic operations and stochastic equations

Journal Article · · Neuromorphic Computing and Engineering

Abstract Non-von Neumann computational hardware, based on neuron-inspired, non-linear elements connected via linear, weighted synapses -- so-called neuromorphic systems -- is a viable computational substrate. Since neuromorphic systems have been shown to use less power than CPUs for many applications, they are of potential use in autonomous systems such as robots, drones, and satellites, for which power resources are at a premium. The power used by neuromorphic systems is approximately proportional to the number of spiking events produced by neurons on-chip. However, typical information encoding on these chips is in the form of firing rates that unarily encode information. That is, the number of spikes generated by a neuron is meant to be proportional to an encoded value used in a computation or algorithm. Unary encoding is less efficient (produces more spikes) than binary encoding. For this reason, here we present neuromorphic computational mechanisms for implementing binary two's complement operations. We use the mechanisms to construct a neuromorphic, binary matrix multiplication algorithm that may be used as a primitive for linear differential equation integration, deep networks, and other standard calculations. We also construct a random walk circuit and apply it in Brownian motion simulations. We study how both algorithms scale in circuit size and iteration time.

Research Organization:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
SL20-ML-Neuromorphic-PD3Rs; 89233218CNA000001
OSTI ID:
1908385
Alternate ID(s):
OSTI ID: 1902429; OSTI ID: 1957912
Report Number(s):
LA-UR-21-22286
Journal Information:
Neuromorphic Computing and Engineering, Journal Name: Neuromorphic Computing and Engineering Vol. 3 Journal Issue: 1; ISSN 2634-4386
Publisher:
IOP PublishingCopyright Statement
Country of Publication:
United Kingdom
Language:
English

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