边缘人工智能:解决效率范式

Colleen P. Bailey, Arthur C. Depoian, Ethan R. Adams
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引用次数: 7

摘要

近年来,大规模深度学习神经网络算法的发展趋势越来越明显。可用计算的快速增长进一步延续了这一运动。虽然这些巨大的模型获得了卓越的性能,但所需的计算成本也相应巨大。因此,需要高效和智能的算法设计,以实现与当前最先进的算法相似的高性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Edge AI: Addressing the Efficiency Paradigm
Recent years have seen a growing trend towards massive deep learning neural network algorithms. This movement is further perpetuated by the rapid growth in available computation. While these giant models attain remarkable performance, the required computational cost is proportionally huge. There is a resulting necessity for efficient and intelligent algorithm design that can achieve similar high performance to current state-of the-art.
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