Fully-Binarized, Parallel, RRAM-based Computing Primitive for In-Memory Similarity Search

In this work, we propose a fully-binarized XOR-based IMSS (In-Memory Similarity Search) using RRAM (Resistive Random Access Memory) arrays. XOR (Exclusive OR) operation is realized using 2T-2R bitcells arranged along the column in an array. This enables simultaneous match operation across multiple s...

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Bibliographic Details
Published in:arXiv.org 2022-09
Main Authors: Kingra, Sandeep Kaur, Parmar, Vivek, Verma, Deepak, Bricalli, Alessandro, Piccolboni, Giuseppe, Molas, Gabriel, Regev, Amir, Suri, Manan
Format: Article
Language:English
Subjects:
Arrays
Energy dissipation
Image classification
Mathematical analysis
Random access memory
Similarity
Online Access:Get full text
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Summary:In this work, we propose a fully-binarized XOR-based IMSS (In-Memory Similarity Search) using RRAM (Resistive Random Access Memory) arrays. XOR (Exclusive OR) operation is realized using 2T-2R bitcells arranged along the column in an array. This enables simultaneous match operation across multiple stored data vectors by performing analog column-wise XOR operation and summation to compute HD (Hamming Distance). The proposed scheme is experimentally validated on fabricated RRAM arrays. Full-system validation is performed through SPICE simulations using open source Skywater 130 nm CMOS PDK demonstrating energy of 17 fJ per XOR operation using the proposed bitcell with a full-system power dissipation of 145 \(\mu\)W. Using projected estimations at advanced nodes (28 nm) energy savings of \(\approx\)1.5\(\times\) compared to the state-of-the-art can be observed for a fixed workload. Application-level validation is performed on HSI (Hyper-Spectral Image) pixel classification task using the Salinas dataset demonstrating an accuracy of 90%.
ISSN:2331-8422
DOI:10.48550/arxiv.2208.02651