Conference Paper

A hardware/application overlay model for large-scale neuromorphic simulation


Neuromorphic computing is gaining momentum as an alternative hardware platform for large-scale neural simulation. However, with several major devices and systems available and planned, often with very different characteristics, it is not always clear which platform is suitable for which application. Simulating the platform on conventional computers is typically too slow to be of use, but an alternative approach is to implement an ‘emulation’ of the hardware in FPGAs which can execute at near-hardware speeds but does not commit to a specific hardware architecture. We present an overlay model - a method which superimposes bespoke features on top of a standard template - in both hardware and software to implement neuromorphic architectures using the POETS (Partially Ordered Event Triggered Systems) system. This combination of overlays permits very large-scale simulations to be performed in real time for hardware exploration or application verification, while retaining the flexibility to redefine either the hardware or software layer, if results indicate potential to improve performance, or significant design problems. Using this system we simulate up to 500,000 neurons on a single-box system, that can be scaled to ∼4,000,000 neurons in an 8-box configuration. Results indicate the crucial constraint for real-time simulation: peak input spike rate per neuron; and help to optimise both hardware and software around neural application requirements. The preliminary architecture demonstrates the feasibility of an overlay model, while indicating directions for future neuromorphic systems. With POETS, we introduce a platform that can help to shape and investigate the neuromorphic architectures of the future.

Attached files


Rast, Alexander
Shahsavari, Mahyar
Bragg, Graeme M.
Vousden, Mark L.
Thomas, David
Brown, Andrew

Oxford Brookes departments

School of Engineering, Computing and Mathematics


Year of publication: 2020
Date of RADAR deposit: 2020-05-04

“© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”

Related resources

This RADAR resource is the Accepted Manuscript of A hardware/application overlay model for large-scale neuromorphic simulation
This RADAR resource is Part of Proceedings. 2020 International Joint Conference on Neural Networks (IJCNN) [ISBN: 9781728169262] / edited by (ISBN:


  • Owner: Joseph Ripp
  • Collection: Outputs
  • Version: 1 (show all)
  • Status: Live
  • Views (since Sept 2022): 139