Encoding Integers and Rationals on Neuromorphic Computers using Virtual Neuron

TL;DR

This paper introduces the virtual neuron as an efficient encoding mechanism for integers and rationals on neuromorphic computers, demonstrating its energy efficiency and utility in basic computational functions.

Contribution

It proposes the virtual neuron for number encoding on neuromorphic hardware and evaluates its performance and applicability for general-purpose computation.

Findings

Performs addition with 23 nJ energy consumption.

Effective in mu-recursive functions.

Validated on physical and simulated hardware.

Abstract

Neuromorphic computers perform computations by emulating the human brain, and use extremely low power. They are expected to be indispensable for energy-efficient computing in the future. While they are primarily used in spiking neural network-based machine learning applications, neuromorphic computers are known to be Turing-complete, and thus, capable of general-purpose computation. However, to fully realize their potential for general-purpose, energy-efficient computing, it is important to devise efficient mechanisms for encoding numbers. Current encoding approaches have limited applicability and may not be suitable for general-purpose computation. In this paper, we present the virtual neuron as an encoding mechanism for integers and rational numbers. We evaluate the performance of the virtual neuron on physical and simulated neuromorphic hardware and show that it can perform an addition operation using 23 nJ of energy on average using a mixed-signal memristor-based neuromorphic processor. We also demonstrate its utility by using it in some of the mu-recursive functions, which are the building blocks of general-purpose computation.