Hyperdimensional computing using time-to-spike neuromorphic circuits

Bent, Graham, Simpkin, Christopher, Li, Yuhua ORCID: https://orcid.org/0000-0003-2913-4478 and Preece, Alun ORCID: https://orcid.org/0000-0003-0349-9057 2022. Hyperdimensional computing using time-to-spike neuromorphic circuits. Presented at: 2022 IEEE World Congress on Computational Intelligence (WCCI), Padova, Italy, 18-23 July 2022. International Joint Conference on Neural Networks. IEEE,

Preview

PDF - Accepted Post-Print Version Download (3MB) | Preview

Abstract

Vector Symbolic Architectures (VSA) can be used to encode complex objects, such as services and sensors, as hypervectors. Such hypervectors can be used to perform efficient distributed service discovery and workflow orchestration in communications constrained environments typical of the Internet of Things (IoT). In these environments, energy efficiency is of great importance. However, most hypervector representations use dense i.i.d element values and performing energy efficient hyperdimensional computing operations on such dense vectors is challenging. More recently, a sparse binary VSA scheme has been proposed based on a slot encoding having M slots with B bit positions per slot, in which only one bit per slot can be set. This paper shows for the first time that such sparse encoded hypervectors can be mapped into energy-efficient time-to-spike Spiking Neural Network (SNN) circuits, such that all the required VSA operations can be performed. Example VSA SNN circuits have been implemented in the Brian 2 SNN simulator, showing that all VSA binding, bundling, unbinding, and clean-up memory operations execute correctly. Based on these circuit implementations, estimates of the energy and processing time required to perform the different VSA operations on typical SNN neuromorphic devices are estimated. Recommendations for the design of future SNN neuromorphic processor hardware that can more efficiently perform VSA processing are also made.

Item Type:	Conference or Workshop Item (Paper)
Status:	Unpublished
Schools:	Computer Science & Informatics Data Innovation Research Institute (DIURI) Crime and Security Research Institute (CSURI)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords:	Vector Symbolic Architecture, Hypervectors, Spiking Neural Networks, Neuromorphic Processing
Publisher:	IEEE
Funders:	U.K. Ministry of Defence
Date of First Compliant Deposit:	6 June 2022
Date of Acceptance:	26 April 2022
Last Modified:	30 Jul 2023 16:53
URI:	https://orca.cardiff.ac.uk/id/eprint/150097

Citation Data

Cited 2 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item