Stochastic optimisation of lookup table networks, for realtime inference on embedded systems

Kiefer, Chris (2021) Stochastic optimisation of lookup table networks, for realtime inference on embedded systems. 2nd Conference on AI Music Creativity (MuMe + CSMC), Online, 18.-22. July 2021. Published in: Proceedings of the 2nd Conference on AI Music Creativity. 1-10. AIMC

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Abstract

Neural networks running on FPGAs offer great potential for creative applications in realtime audio and sensor processing, but training models to run on these platforms can be challenging. Research in TinyML offers methods for transforming trained neural networks to run on embedded systems. Further gains might be made by training networks directly constructed from lookup tables (LUTs), the basic element of FPGA hardware. A novel method, Stochastic Logic Optimisation, is presented for supervised learning with feed-forward networks of LUTs. The method is found to significantly improve on the use of both a genetic algorithm and memorisation in a beat prediction task.

Item Type: Conference Proceedings
Keywords: FPGA, machine learning, embedded computing
Schools and Departments: School of Media, Film and Music > Music
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 28 Jun 2021 08:53
Last Modified: 28 Feb 2022 16:43
URI: http://sro.sussex.ac.uk/id/eprint/100002

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Project NameSussex Project NumberFunderFunder Ref
MIMIC: Musically Intelligent Machines Interacting CreativelyG2434AHRC-ARTS & HUMANITIES RESEARCH COUNCILAH/R002657/1