A 64-TOPS Energy-Efficient Tensor Accelerator in 14nm With Reconfigurable Fetch Network and Processing Fusion for Maximal Data Reuse

For energy-efficient accelerators in data centers that leverage advances in the performance and energy efficiency of recent algorithms, flexible architectures are critical to support state-of-the-art algorithms for various deep learning tasks. Due to the matrix multiplication units at the core of te...

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Váldodahkkit:	Sang Min Lee, Hanjoon Kim, Jeseung Yeon, Juyun Lee, Younggeun Choi, Minho Kim, Changjae Park, Kiseok Jang, Youngsik Kim, Yongseung Kim, Changman Lee, Hyuck Han, Won Eung Kim, Rui Tang, Joon Ho Baek
Materiálatiipa:	Artigo
Giella:	Inglês
Almmustuhtton:	IEEE 2022-01-01
Ráidu:	IEEE Open Journal of the Solid-State Circuits Society
Fáttát:	AI accelerators convolutional neural networks data reuse depth-wise and group convolution inference ML accelerator
Liŋkkat:	https://ieeexplore.ieee.org/document/9927346/
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