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Comparing Neuromorphic Solutions in Action: Implementing a Bio-Inspired Solution to a Benchmark Classification Task

Alan Diamond1, Thomas Nowotny1, Michael Schmuker1

  • 1School of Engineering and Informatics, University of Sussex Brighton, UK.

Frontiers in Neuroscience
|January 19, 2016
PubMed
Summary
This summary is machine-generated.

Neuromorphic computing platforms were evaluated for multivariate classification. While performance was comparable, host-device communication and non-neuronal computations significantly impacted speed and power efficiency.

Keywords:
benchmarkingbioinspiredclassificationneuromorphic hardwarespiking neural networks

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Area of Science:

  • Neuromorphic computing
  • Computational neuroscience
  • Artificial intelligence hardware

Background:

  • Neuromorphic computing utilizes neuronal circuit models for problem-solving.
  • Availability of neuromorphic hardware and development of neuromorphic algorithms are increasing.
  • Assessing and comparing neuromorphic systems is crucial for practical applications.

Purpose of the Study:

  • To practically implement and compare a bio-inspired spiking network for multivariate classification on three distinct neuromorphic platforms.
  • To evaluate performance, ease of implementation, speed, scalability, and power efficiency across different hardware.
  • To identify bottlenecks in neuromorphic system deployment.

Main Methods:

  • Implementation of a spiking neural network for handwritten digit classification.
  • Deployment on three platforms: Spikey (hybrid digital/analog), SpiNNaker (digital spike-based), and GeNN (GPU meta-compiler).
  • Assessment of classification performance, execution speed, and power consumption.

Main Results:

  • Comparable classification performance across all three platforms, indicating model capability over platform limitations.
  • Significant portions of computation time and power consumption occurred on the host machine, not the neuromorphic device.
  • Host-device communication, data preparation, and result decoding were major contributors to overhead.

Conclusions:

  • Specialized neuromorphic hardware benefits can be negated by inefficient host-device communication and non-neuronal computations.
  • Optimization of host-device communication architecture is critical for scalability, throughput, and latency.
  • Minimizing host-device interaction is essential for efficient neuromorphic computing system design.