McDRAM v2: In-Dynamic Random Access Memory Systolic Array Accelerator to Address the Large Model Problem in Deep Neural Networks on the Edge

McDRAM v2: In-Dynamic Random Access Memory Systolic Array Accelerator to Address the Large Model Problem in Deep Neural Networks on the Edge

The energy efficiency of accelerating hundreds of MB-large deep neural networks (DNNs) in a mobile environment is less than that of a server-class big chip accelerator because of the limited power budget, silicon area, and smaller buffer size of static random access memory associated with mobile sys...

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Bibliographic Details
Main Authors: Seunghwan Cho, Haerang Choi, Eunhyeok Park, Hyunsung Shin, Sungjoo Yoo
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Accelerator
convolutional neural network
deep neural network
dynamic random access memory
edge inference
multi-layer perceptron
Online Access:https://ieeexplore.ieee.org/document/9146167/

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