Universal Reconfigurable Hardware Accelerator for Sparse Machine Learning Predictive Models

Universal Reconfigurable Hardware Accelerator for Sparse Machine Learning Predictive Models

This study presents a universal reconfigurable hardware accelerator for efficient processing of sparse decision trees, artificial neural networks and support vector machines. The main idea is to develop a hardware accelerator that will be able to directly process sparse machine learning models, resu...

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Bibliographic Details
Main Authors: Vuk Vranjkovic, Predrag Teodorovic, Rastislav Struharik
Format: Article
Language:English
Published: MDPI AG 2022-04-01
Series:Electronics
Subjects:
decision trees
support vector machines
artificial neural networks
hardware accelerator architectures
edge computing
sparse predictive models
Online Access:https://www.mdpi.com/2079-9292/11/8/1178

Internet

https://www.mdpi.com/2079-9292/11/8/1178

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