A Survey of ReRAM-based Architectures for Processing-in-memory and Neural Networks

Sparsh Mittal
Indian Institute of Technology Hyderabad
Machine Learning and Knowledge Extraction 2018









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As data movement operations and power-budget become key bottlenecks in the design of computing systems, the interest in unconventional approaches such as processing-in-memory (PIM) and machine learning (ML), especially neural network (NN) based accelerators has grown significantly. Resistive RAM (ReRAM) is a promising technology for efficiently architecting PIM and NN based accelerators due to its capabilities to work as both: high-density/low-energy storage and in-memory computation/search engine. In this paper, we present a survey of techniques for designing ReRAM-based PIM and NN architectures. By classifying the techniques based on key parameters, we underscore their similarities and differences. This paper will be valuable for computer architects, chip designers and researchers in the area of machine learning.
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