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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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Open Journal of the Solid-State Circuits Society
Subjects:
AI accelerators
convolutional neural networks
data reuse
depth-wise and group convolution
inference
ML accelerator
Online Access:https://ieeexplore.ieee.org/document/9927346/

Internet

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

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