AstroByte: A multi-FPGA Architecture for Accelerated Simulations of Fault-tolerant Spiking Astrocyte-Neuron Networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution


— Spiking astrocyte neural networks (SANN) are a new
computational paradigm that exhibit enhanced self-adapting and
reliability properties. The inclusion of astrocyte behaviour
increases the computational load and critically the number of
connections, where each astrocyte typically communicates with 6-9
neurons (and their associated synapses) with feedback pathways
from each neuron to the astrocyte. Each astrocyte cell also
communicates with its neighbouring cell resulting in a significant
interconnect density. The substantial level of parallelisms in
SANNs lends itself to acceleration in hardware, however, the
challenge in accelerating simulations of SANNs firmly resides in
scalable interconnect and the ability to inject and retrieve data
from the hardware. This paper presents a novel multi-FPGA
acceleration architecture, AstroByte, for the speedup of SANNs.
AstroByte explores Networks-on-Chip (NoC) routing mechanisms
to address the challenge of communicating both spike event
(neuron data) and numeric (astrocyte data) across significant
interconnect pathways between astrocytes and neurons. AstroByte
also exploits the NoC interconnect to inject data and retrieve
runtime data from the accelerated SANN simulations. Results will
show that AstroByte can simulate SANN applications with speedup
factors of between x162 -x188 over Matlab equivalent simulations.
Original languageEnglish
Title of host publicationDesign, Automation and Test in Europe Conference (DATE)
Number of pages6
Publication statusAccepted/In press - 30 Oct 2019


  • SANN acceleration
  • multi-FPGA design
  • Data Acquisition
  • Networks on Chip
  • astrocyte

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