期刊
2019 IEEE COMPUTER SOCIETY ANNUAL SYMPOSIUM ON VLSI (ISVLSI 2019)
卷 -, 期 -, 页码 555-561出版社
IEEE COMPUTER SOC
DOI: 10.1109/ISVLSI.2019.00105
关键词
pre-processing; pruning; quantization; DNN; accelerator; hardware; software; performance; energy efficiency; low power; deep learning; neural networks; edge computing; IoT
In the Machine Learning era, Deep Neural Networks (DNNs) have taken the spotlight, due to their unmatchable performance in several applications, such as image processing, computer vision, and natural language processing. However, as DNNs grow in their complexity, their associated energy consumption becomes a challenging problem. Such challenge heightens for edge computing, where the computing devices are resource-constrained while operating on limited energy budget. Therefore, specialized optimizations for deep learning have to be performed at both software and hardware levels. In this paper, we comprehensively survey the current trends of such optimizations and discuss key open research mid-term and long-term challenges.
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