High performance computing on SpiNNaker neuromorphic platform: A case study for energy efficient image processing

Sugiarto, Indar and Liu, Gengting and Davidson, Simon and Plana, Luis A. and Furber, Steve Bryan (2016) High performance computing on SpiNNaker neuromorphic platform: A case study for energy efficient image processing. In: 2016 IEEE 35th International Performance Computing and Communications Conference (IPCCC), 11-12-2016 - 11-12-2016, Las Vegas - USA.

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Abstract

This paper presents an efficient strategy to implement parallel and distributed computing for image processing on a neuromorphic platform. We use SpiNNaker, a many-core neuromorphic platform inspired by neural connectivity in the brain, to achieve fast response and low power consumption. Our proposed method is based on fault-tolerant finegrained parallelism that uses SpiNNaker resources optimally for process pipelining and decoupling. We demonstrate that our method can achieve a performance of up to 49.7 MP/J for Sobel edge detector, and can process 1600 � 1200 pixel images at 697 fps. Using simulated Canny edge detector, our method can achieve a performance of up to 21.4 MP/J. Moreover, the framework can be extended further by using larger SpiNNaker machines. This will be very useful for applications such as energy-aware and time-critical-mission robotics as well as very high resolution computer vision systems.

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