EMAXVR: A programmable accelerator employing near ALU utilization to DSA

The domain specific accelerators (DSAs) have appeared remarkable performances in many real-world applications such as deep learning. However, the benefit on performances is somehow eaten up by the increasing development cost and poor programmability. In this paper, a programmable accelerator is prop...

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Published in2018 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS) pp. 1 - 3
Main Authors Ichikura, Takahiro, Yamano, Ryusuke, Kikutani, Yuma, Zhang, Renyuan, Nakashima, Yasuhiko
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2018
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Summary:The domain specific accelerators (DSAs) have appeared remarkable performances in many real-world applications such as deep learning. However, the benefit on performances is somehow eaten up by the increasing development cost and poor programmability. In this paper, a programmable accelerator is proposed by employing near ALU utilization to DSA, which is an improved version of our previously reported accelerator called EMAXV. As a result, we found our programmable accelerator can compute convolution operations in AlexNet with only 17% lower utilization of ALU and 14% upper rate of data reuse compared with the latest flexible DSA.
ISSN:2473-4683
DOI:10.1109/CoolChips.2018.8373078