Recovering accuracy of RRAM-based CIM for binarized neural network via Chip-in-the-loop training
Resistive random access memory (RRAM) based computing-in-memory (CIM) is attractive for edge artificial intelligence (AI) applications, thanks to its excellent energy efficiency, compactness and high parallelism in matrix vector multiplication (MatVec) operations. However, existing RRAM-based CIM de...
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Main Authors: | , , , , |
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Format: | Conference or Workshop Item |
Language: | English |
Published: |
2022
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/159308 |
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Institution: | Nanyang Technological University |
Language: | English |
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