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Diamond, Nowotny, Schmuker - 2016 - Comparing Neuromorphic Solutions in Action Implementing a Bio-Inspired Solution to a Benchmark Class.pdf (2.15 MB)

Comparing neuromorphic solutions in action: implementing a bio-inspired solution to a benchmark classification task on three parallel-computing platforms

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posted on 2023-06-09, 00:10 authored by Alan Diamond, Thomas NowotnyThomas Nowotny, Michael SchmukerMichael Schmuker
Neuromorphic computing employs models of neuronal circuits to solve computing problems. Neuromorphic hardware systems are now becoming more widely available and “neuromorphic algorithms” are being developed. As they are maturing toward deployment in general research environments, it becomes important to assess and compare them in the context of the applications they are meant to solve. This should encompass not just task performance, but also ease of implementation, speed of processing, scalability, and power efficiency. Here, we report our practical experience of implementing a bio-inspired, spiking network for multivariate classification on three different platforms: the hybrid digital/analog Spikey system, the digital spike-based SpiNNaker system, and GeNN, a meta-compiler for parallel GPU hardware. We assess performance using a standard hand-written digit classification task. We found that whilst a different implementation approach was required for each platform, classification performances remained in line. This suggests that all three implementations were able to exercise the model’s ability to solve the task rather than exposing inherent platform limits, although differences emerged when capacity was approached. With respect to execution speed and power consumption, we found that for each platform a large fraction of the computing time was spent outside of the neuromorphic device, on the host machine. Time was spent in a range of combinations of preparing the model, encoding suitable input spiking data, shifting data, and decoding spike-encoded results. This is also where a large proportion of the total power was consumed, most markedly for the SpiNNaker and Spikey systems. We conclude that the simulation efficiency advantage of the assessed specialized hardware systems is easily lost in excessive host-device communication, or non-neuronal parts of the computation. These results emphasize the need to optimize the host-device communication architecture for scalability, maximum throughput, and minimum latency. Moreover, our results indicate that special attention should be paid to minimize host-device communication when designing and implementing networks for efficient neuromorphic computing.

Funding

Human Brain Project: Neuromorphic Implementations of Multivariate Classification Inspired by the Olfactory System; G1359; EUROPEAN UNION; 604102 HBP NEUROCLASSIOS

Biomachinelearning: Bio-inspired Machine Learning for Chemical Sensing (fellow: Michael Schmuker); G1382; EUROPEAN UNION; PIEF-GA-2012-331892

History

Publication status

  • Published

File Version

  • Published version

Journal

Frontiers in Neuroscience

ISSN

1662-4548

Publisher

Frontiers

Issue

a491

Volume

9

Page range

1-14

Department affiliated with

  • Informatics Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2016-01-28

First Open Access (FOA) Date

2016-01-28

First Compliant Deposit (FCD) Date

2016-01-28

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