Davidson, Simon

Furber, Stephen

Liu, Gengting

Plana, Luis A.

Sugiarto, Indar

English

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

The University of Manchester - Institutional Repository

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

https://pure.manchester.ac.uk/ws/files/51830495/High_Performance_Computing_on_SpiNNaker_Neuromorphic_Platform_a_Case_Study_for_Energy_Efficient_Image_Processin.pdf

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

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The University of Manchester - Institutional Repository