High performance computing on SpiNNaker neuromorphic platform: A case study for energy efficient image processing
| Autor: | Luis A. Plana, Indar Sugiarto, Steve Furber, Gengting Liu, Simon Davidson |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Multi-core processor
business.industry Computer science Detector Sobel operator Robotics Image processing 010103 numerical & computational mathematics 02 engineering and technology Supercomputer 01 natural sciences 020202 computer hardware & architecture Neuromorphic engineering 0202 electrical engineering electronic engineering information engineering Canny edge detector Artificial intelligence 0101 mathematics business Computer hardware |
| Zdroj: | Sugiarto, I, Liu, G, Davidson, S, Plana, L A & Furber, S 2016, High performance computing on SpiNNaker neuromorphic platform: A case study for energy efficient image processing . in Performance Computing and Communications Conference (IPCCC), 2016 IEEE 35th International : IEEE . https://doi.org/10.1109/PCCC.2016.7820645 IPCCC |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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