A real-time hybrid neuron network for highly parallel cognitive systems

Conference paper (2016)

Authors

G.J. Christiaanse Education and Research Support
Amir Zjajo Signal Processing Systems - EEMCS
C. Galuzzi Universiteit Maastricht
T.G.R.M. van Leuken Signal Processing Systems - EEMCS
Research Group
Signal Processing Systems (EEMCS) (TU Delft)
Copyright: © 2016 G.J. Christiaanse, Amir Zjajo, C. Galuzzi, T.G.R.M. van Leuken
DOI
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https://doi.org/10.1109/embc.2016.7590820
More Info
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Published Date

18-10-2016

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

Abstract

For comprehensive understanding of how neurons communicate with each other, new tools need to be developed that can accurately mimic the behaviour of such neurons and neuron networks under `real-time' constraints. In this paper, we propose an easily customisable, highly pipelined, neuron network design, which executes optimally scheduled floating-point operations for maximal amount of biophysically plausible neurons per FPGA family type. To reduce the required amount of resources without adverse effect on the calculation latency, a single exponent instance is used for multiple neuron calculation operations. Experimental results indicate that the proposed network design allows the simulation of up to 1188 neurons on Virtex7 (XC7VX550T) device in brain real-time yielding a speed-up of x12.4 compared to the state-of-the art.