Time-multiplexed System-on-Chip using Fault-tolerant Astrocyte-Neuron Networks

Anju Johnson, Junxiu Liu, Alan Millard, Shvan Karim, Andy Tyrrell, Jim Harkin, Jon Timmis, Liam McDaid, David Halliday

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)


Spike-based brain-inspired systems have shown an immense capability to achieve internal stability, widely referred to as homeostasis. This ability enrols them as the best candidate for next-generation computational neuroscience as they bridge the gap between neuroscience and machine learning. Spiking Neural Networks (SNN), a third generation Artificial Neural Network (ANN), which operates using discrete events of spikes, contributes to a category of biologically-realistic models of neurons to carry out computations. Spiking Astrocyte-Neuron Networks (SANN) have a characteristic attribute homologous to brain self-repair. Although SNNs are more powerful in theory than 2nd generation ANNs, they are not widely in use as their implementations on normal hardware are computationally-intensive. On the contrary, due to the capability of modern hardware such as FPGAs, which operates in MHz and GHz range, facilitates real-time and faster-than-real-time simulations of SNNs. In this work, we overcome the computational overhead of the SNNs using the benefits of real-time hardware computations, utilizing time-multiplexing to design a Self-rePairing spiking Astrocyte Neural NEtwoRk (SPANNER) chip, generic to users` choice of task, emphasizing fault-tolerance, targeting safety-critical applications. We demonstrate the proposed methodology on a SANN system implemented on Xilinx Artix-7 FPGA. The proposed architecture has minimal hardware footprints, power dissipation profile and real-time computational capability, enhancing its usability in constrained applications.
Original languageEnglish
Title of host publicationProceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
EditorsSuresh Sundaram
Number of pages8
ISBN (Electronic)9781538692769
ISBN (Print)9781538692776
Publication statusPublished - 31 Jan 2019
Externally publishedYes
EventIEEE Symposium on Computational Intelligence - Bengaluru, India
Duration: 18 Nov 201821 Nov 2018
http://ieee-ssci2018.org/ (Link to Conference Website)


ConferenceIEEE Symposium on Computational Intelligence
Abbreviated titleIEEE-SSCI 2018
Internet address


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