Timely: Pushing data movements and interfaces in pim accelerators towards local and in time domain
Resistive-random-access-memory (ReRAM) based processing-in-memory (R2PIM)
accelerators show promise in bridging the gap between Internet of Thing devices'
constrained resources and Convolutional/Deep Neural Networks'(CNNs/DNNs') prohibitive
energy cost. Specifically, R2PIM accelerators enhance energy efficiency by eliminating the
cost of weight movements and improving the computational density through ReRAM's high
density. However, the energy efficiency is still limited by the dominant energy cost of input …
accelerators show promise in bridging the gap between Internet of Thing devices'
constrained resources and Convolutional/Deep Neural Networks'(CNNs/DNNs') prohibitive
energy cost. Specifically, R2PIM accelerators enhance energy efficiency by eliminating the
cost of weight movements and improving the computational density through ReRAM's high
density. However, the energy efficiency is still limited by the dominant energy cost of input …
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