An Efficient and Fast Softmax Hardware Architecture (EFSHA) for Deep Neural Networks

Muhammad Awais Hussain, Tsung Han Tsai

研究成果: 書貢獻/報告類型會議論文篇章同行評審

3 引文 斯高帕斯(Scopus)

摘要

Deep neural networks are widely used in computer vision applications due to their high performance. However, DNNs involve a large number of computations in the training and inference phase. Among the different layers of a DNN, the softmax layer has one of the most complex computations as it involves exponent and division operations. So, a hardware-efficient implementation is required to reduce the on-chip resources. In this paper, we propose a new hardware-efficient and fast implementation of the softmax activation function. The proposed hardware implementation consumes fewer hardware resources and works at high speed as compared to the state-of-the-art techniques.

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主出版物標題2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781665419130
DOIs
出版狀態已出版 - 6 6月 2021
事件3rd IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021 - Washington, United States
持續時間: 6 6月 20219 6月 2021

出版系列

名字2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021

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???event.eventtypes.event.conference???3rd IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021
國家/地區United States
城市Washington
期間6/06/219/06/21

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