Correctness-preserving Compression of Datasets and Neural Network Models

Neural networks deployed on edge devices must be efficient both in terms of their model size and the amount of data movement they cause when classifying inputs. These efficiencies are typically achieved through model compression: pruning a fully trained network model by zeroing out the weights. Give...

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Vydané v:	2020 IEEE/ACM 4th International Workshop on Software Correctness for HPC Applications (Correctness) s. 1 - 9
Hlavní autori:	Joseph, Vinu, Chalapathi, Nithin, Bhaskara, Aditya, Gopalakrishnan, Ganesh, Panchekha, Pavel, Zhang, Mu
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.11.2020
Predmet:	Bayes methods Condensa Correctness Verification correctness-preserving compression data compression data compression algorithms Data models data movement dataset compression decompressed data fully trained network model image coding JPEG learning learning (artificial intelligence) Machine Learning Model Compression neural nets neural network correctness Neural networks Optimization Semantics Training ZFP
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