Publicação

Pruning weightless neural networks

Detalhes bibliográficos
Resumo:	Weightless neural networks (WNNs) are a type of machine learning model which perform prediction using lookup tables (LUTs) instead of arithmetic operations. Recent advancements in WNNs have reduced model sizes and improved accuracies, reducing the gap in accuracy with deep neural networks (DNNs). Modern DNNs leverage “pruning” techniques to reduce model size, but this has not previously been explored for WNNs. We propose a WNN pruning strategy based on identifying and culling the LUTs which contribute least to overall model accuracy. We demonstrate an average 40% reduction in model size with at most 1% reduction in accuracy.
Autores principais:	Susskind, Z.
Outros Autores:	Bacellar, A. T. L.; Arora, A.; Villon, L. A. Q.; Mendanha, R.; Araújo, L. S. de.; Dutra, D. L. C.; Lima, P. M. V.; França, F. M. G.; Miranda, I. D. S.; Breternitz Jr., M.; John, L. K.
Ano:	2022
País:	Portugal
Tipo de documento:	documento de conferência
Tipo de acesso:	acesso aberto
Instituição associada:	ISCTE
Idioma:	inglês
Origem:	Repositório ISCTE

Descrição
Resumo:	Weightless neural networks (WNNs) are a type of machine learning model which perform prediction using lookup tables (LUTs) instead of arithmetic operations. Recent advancements in WNNs have reduced model sizes and improved accuracies, reducing the gap in accuracy with deep neural networks (DNNs). Modern DNNs leverage “pruning” techniques to reduce model size, but this has not previously been explored for WNNs. We propose a WNN pruning strategy based on identifying and culling the LUTs which contribute least to overall model accuracy. We demonstrate an average 40% reduction in model size with at most 1% reduction in accuracy.