Energy Efficiency of Machine Learning in Embedded Systems Using Neuromorphic Hardware
| Autor: | Moonju Park, Minseon Kang, Yongseok Lee |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer Networks and Communications
Computer science Graphics processing unit lcsh:TK7800-8360 Machine learning computer.software_genre Bottleneck embedded system hardware acceleration Electrical and Electronic Engineering neuromorphic processor Artificial neural network business.industry lcsh:Electronics Process (computing) power consumption artificial intelligence Neuromorphic engineering Hardware and Architecture Control and Systems Engineering Embedded system Signal Processing Hardware acceleration Central processing unit Artificial intelligence business computer Efficient energy use |
| Zdroj: | Electronics Volume 9 Issue 7 Electronics, Vol 9, Iss 1069, p 1069 (2020) |
| ISSN: | 2079-9292 |
| DOI: | 10.3390/electronics9071069 |
| Popis: | Recently, the application of machine learning on embedded systems has drawn interest in both the research community and industry because embedded systems located at the edge can produce a faster response and reduce network load. However, software implementation of neural networks on Central Processing Units (CPUs) is considered infeasible in embedded systems due to limited power supply. To accelerate AI processing, the many-core Graphics Processing Unit (GPU) has been a preferred device to the CPU. However, its energy efficiency is not still considered to be good enough for embedded systems. Among other approaches for machine learning on embedded systems, neuromorphic processing chips are expected to be less power-consuming and overcome the memory bottleneck. In this work, we implemented a pedestrian image detection system on an embedded device using a commercially available neuromorphic chip, NM500, which is based on NeuroMem technology. The NM500 processing time and the power consumption were measured as the number of chips was increased from one to seven, and they were compared to those of a multicore CPU system and a GPU-accelerated embedded system. The results show that NM500 is more efficient in terms of energy required to process data for both learning and classification than the GPU-accelerated system or the multicore CPU system. Additionally, limits and possible improvement of the current NM500 are identified based on the experimental results. |
| Databáze: | OpenAIRE |
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