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Rescuing RRAM-Based Computing From Static and Dynamic Faults.
- Source :
-
IEEE Transactions on Computer-Aided Design of Integrated Circuits & Systems . Oct2021, Vol. 40 Issue 10, p2049-2062. 14p. - Publication Year :
- 2021
-
Abstract
- Emerging resistive random access memory (RRAM) has shown the great potential of in-memory processing capability, and thus attracts considerable research interests in accelerating memory-intensive applications, such as neural networks (NNs). However, the accuracy of RRAM-based NN computing can degrade significantly, due to the intrinsic statistical variations of the resistance of RRAM cells. In this article, we propose SIGHT, a synergistic algorithm-architecture fault-tolerant framework, to holistically address this issue. Specifically, we consider three major types of faults for RRAM computing: 1) nonlinear resistance distribution; 2) static variation; and 3) dynamic variation. From the algorithm level, we propose a resistance-aware quantization to compel the NN parameters to follow the exact nonlinear resistance distribution as RRAM, and introduce an input regulation technique to compensate for RRAM variations. We also propose a selective weight refreshing scheme to address the dynamic variation issue that occurs at runtime. From the architecture level, we propose a general and low-cost architecture accordingly for supporting our fault-tolerant scheme. Our evaluation demonstrates almost no accuracy loss for our three fault-tolerant algorithms, and the proposed SIGHT architecture incurs performance overhead as little as 7.14%. [ABSTRACT FROM AUTHOR]
- Subjects :
- *NONVOLATILE random-access memory
Subjects
Details
- Language :
- English
- ISSN :
- 02780070
- Volume :
- 40
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Computer-Aided Design of Integrated Circuits & Systems
- Publication Type :
- Academic Journal
- Accession number :
- 153710615
- Full Text :
- https://doi.org/10.1109/TCAD.2020.3037316