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

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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Detalhes bibliográficos
Principais autores: 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
Formato: Artigo
Idioma:English
Publicado em: IEEE 2022-01-01
coleção:IEEE Open Journal of the Solid-State Circuits Society
Assuntos:
AI accelerators
convolutional neural networks
data reuse
depth-wise and group convolution
inference
ML accelerator
Acesso em linha:https://ieeexplore.ieee.org/document/9927346/

Internet

https://ieeexplore.ieee.org/document/9927346/

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