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

Diamond, Alan, Nowotny, Thomas and Schmuker, michael (2016) Comparing neuromorphic solutions in action: implementing a bio-inspired solution to a benchmark classification task on three parallel-computing platforms. Frontiers in Neuroscience, 9. ISSN 1662-4548

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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.

Item Type: Article
Keywords: neuromorphic hardware, benchmarking, bioinspired, spiking neural networks, classificationhardware,org,spiking neural networks,www
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > Q Science (General) > Q0300 Cybernetics
Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Depositing User: michael Schmuker
Date Deposited: 28 Jan 2016 13:24
Last Modified: 06 Mar 2017 16:47

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Project NameSussex Project NumberFunderFunder Ref
Human Brain Project: Neuromorphic Implementations of Multivariate Classification Inspired by the Olfactory SystemG1359EUROPEAN UNION604102 HBP NEUROCLASSIOS
Biomachinelearning: Bio-inspired Machine Learning for Chemical Sensing (fellow: Michael Schmuker)G1382EUROPEAN UNIONPIEF-GA-2012-331892