Neural-PIM: Efficient Processing-In-Memory with Neural Approximation of Peripherals
Abstract
Processing-in-memory (PIM) architectures have demonstrated great potential in accelerating numerous deep learning tasks. Particularly, resistive random-access memory (RRAM) devices provide a promising hardware substrate to build PIM accelerators due to their abilities to realize efficient in-situ vector-matrix multiplications (VMMs). However, existing PIM accelerators suffer from frequent and energy-intensive analog-to-digital (A/D) conversions,...
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Title
Neural-PIM: Efficient Processing-In-Memory with Neural Approximation of
Peripherals
DOI
Published Date
Jan 30, 2022
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