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Fault Tolerance for RRAM-Based Matrix Operations

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Authors
Liu, Mengyun
Xia, Lixue
Wang, Yu
Chakrabarty, K
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Abstract
An RRAM-based computing system (RCS) provides an energy efficient hardware implementation of vector-matrix multiplication for machine-learning hardware. However, it is vulnerable to faults due to the immature RRAM fabrication process. We propose an efficient fault tolerance method for RCS; the proposed method, referred to as extended-ABFT (X-ABFT), is inspired by algorithm-based fault tolerance (ABFT). We utilize row checksums and test-input vectors to extract signatures for fault detection and error correction. We present a solution to alleviate the overflow problem caused by the limited number of voltage levels for the test-input signals. Simulation results show that for a Hopfield classifier with faults in 5% of its RRAM cells, X-ABFT allows us to achieve nearly the same classification accuracy as in the fault-free case.
Type
Report
Subject
ABFT
fault detection
fault tolerance
neuromorphic computing
RRAM
Permalink
https://hdl.handle.net/10161/17428
Citation
Liu, Mengyun; Xia, Lixue; Wang, Yu; & Chakrabarty, K (n.d.). Fault Tolerance for RRAM-Based Matrix Operations. Retrieved from https://hdl.handle.net/10161/17428.
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Chakrabarty

Krishnendu Chakrabarty

John Cocke Distinguished Professor of Electrical and Computer Engineering
Krishnendu Chakrabarty is the John Cocke Distinguished Professor of Electrical and Computer Engineering and Professor of Computer Science at Duke University.

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