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- W4312036141 abstract "Acceleration, power reduction, reliability and low-overhead are four major goals of neural networks. Residue number systems (RNS) intuitionally can improve all of the goals simultaneously. Especially for reliability-critical applications, the generic ability of modularization empowers the redundant RNS (RRNS) fault tolerance. However, the RRNS still suffers four critical issues including dynamic range (DR), sign detection, limited efficient moduli, and redundancy-decoding complexity. In this paper, we apply the piecewise linear units to limit the DR of next layer, stored sign-and-parity bits to make sign detection efficient, the hierarchical structure to make the best of efficient moduli, and the AN-codes for both sub-RNS error cor-rection and MMR checking. An expanded residue-to-binary converter is developed for highly reduction of the whole multi-ple-module redundancy decoder. This is the first paper to incor-porate AN codes to the RRNS applied in highly-reliable neural networks. From our evaluation under approximately equal bit-length L of moduli in a DR of hkL bits, the <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$g=(hk+2)(hk+1)/2$</tex> residue-to-binary converters and g(g-1)/2 comparators in the conventional RRNS can be reduced to an expanded residue-to-binary converter with only k decoders. From the BLER anal-yses, more than 126 times of MTBF can be achieved." @default.
- W4312036141 created "2023-01-04" @default.
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- W4312036141 date "2022-11-01" @default.
- W4312036141 modified "2023-10-16" @default.
- W4312036141 title "AN-HRNS: AN-Coded Hierarchical Residue Number System for Reliable Neural Network Accelerators" @default.
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- W4312036141 doi "https://doi.org/10.1109/ats56056.2022.00035" @default.
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