A new review paper titled “Compute in-Memory with Non-Volatile Elements for Neural Networks: A Review from a Co-Design Perspective” was published by researchers at Argonne National Lab, Purdue University, and Indian Institute of Technology Madras.
“With an over-arching co-design viewpoint, this review assesses the use of cross-bar based CIM for neural networks, connecting the material properties and the associated design constraints and demands to application, architecture, and performance. Both digital and analog memory are considered, assessing the status for training and inference, and providing metrics for the collective set of properties non-volatile memory materials will need to demonstrate for a successful CIM technology,” states the paper.
Find the technical paper here. Published December 2022.
Haensch, W., Raghunathan, A., Roy, K., Chakrabarti, B., Phatak, C. M., Wang, C., & Guha, S. (2022). Compute in‐Memory with Non‐Volatile Elements for Neural Networks: A Review from a Co‐Design Perspective. Advanced Materials, 2204944.
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