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• W3084937280 endingPage "101887" @default.
• W3084937280 startingPage "101887" @default.
• W3084937280 abstract "Convolutional neural networks (CNNs) have proven to be a disruptive technology in most vision, speech and image processing tasks. Given their ubiquitous acceptance, the research community is investing a lot of time and resources on deep neural networks. Custom hardware such as ASICs are proving to be extremely worthy platforms for running such programs. However, the ever-increasing complexity of these algorithms poses challenges in achieving real-time performance. Specifically, CNNs have prohibitive costs in terms of computation time, throughput, latency, storage space, memory bandwidth, and power consumption. Hence, in the last 5 years, a lot of work has been done by the scientific community to mitigate these costs. Researchers have primarily focused on reducing the computation time, the number of computations, the memory access time, and the size of the memory footprint. In this survey paper, we propose a novel taxonomy to classify prior work, and describe some of the key contributions in these areas in detail." @default.
• W3084937280 created "2020-09-21" @default.
• W3084937280 creator A5007225479 @default.
• W3084937280 creator A5011936649 @default.
• W3084937280 creator A5071424693 @default.
• W3084937280 date "2021-02-01" @default.
• W3084937280 modified "2023-10-07" @default.
• W3084937280 title "Accelerating CNN Inference on ASICs: A Survey" @default.
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• W3084937280 doi "https://doi.org/10.1016/j.sysarc.2020.101887" @default.
• W3084937280 hasPublicationYear "2021" @default.
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