Bi-modal accuracy distribution in quantisation aware training of SNNs : an investigation
Pannir Selvam, Durai Arun and Wilmshurst, Alan and Thomas, Kevin and Di Caterina, Gaetano; (2024) Bi-modal accuracy distribution in quantisation aware training of SNNs : an investigation. In: SPIE Sensors + Imaging. SPIE, GBR. (In Press)
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Abstract
Understanding the caveats of deploying a Spiking Neural Networks (SNNs) in an embedded system is important, due to their potential to achieve high efficiency in applications using event-based data. This work investigates the effects of the quantisation of SNNs from the perspective of deploying a model onto FPGAs. This paper attempts to identify whether the decrease in accuracy is consistent across different models. Three SNN models were trained using Quantisation-aware training (QAT). In addition, three different types of quantisation were applied on all three models. Further, these models are trained while they are represented through various custom bit-depths using Brevitas. Then, the performance metric curves such as accuracy, training loss, and test loss resulted from QAT were viewed as performance distribution, to show that the significant accuracy drop found in these curves manifests itself as a bi-modal distribution This work then investigates whether the decrease in accuracy is consistent across different models.
ORCID iDs
Pannir Selvam, Durai Arun ORCID: https://orcid.org/0000-0002-3190-2037, Wilmshurst, Alan, Thomas, Kevin and Di Caterina, Gaetano ORCID: https://orcid.org/0000-0002-7256-0897;-
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Item type: Book Section ID code: 90479 Dates: DateEvent30 June 2024Published30 June 2024AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 04 Sep 2024 14:37 Last modified: 22 Sep 2024 00:40 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/90479