Edge Deployable Distributed Evolutionary Optimization based Calibration method for Neural Quantization

Accuracy drop in neural quantization is addressed in prior-art through Post Training Quantization (PTQ) schemes such as Percentile and Range-based calibration that remain sensitive to the data distribution. On the other hand, the sophisticated methods that efficiently handle the variability in data...

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Vydané v:	Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 6240 - 6244
Hlavní autori:	Tiwari, Utsav, Miriyala, Srinivas S, Rajendiran, Vikram N
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 14.04.2024
Predmet:	Block-wise calibration Calibration Deep Neural Networks Evolutionary algorithm Memory management On-Device deployment Performance evaluation Post Training Quantization Programming Quantization (signal) Signal processing algorithms Training
ISSN:	2379-190X
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