Dependability of Alternative Computing Paradigms for Machine Learning: hype or hope?
2022 25th International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS)（2022）
Abstract
Today we observe amazing performance achieved by Machine Learning (ML); for specific tasks it even surpasses human capabilities. Unfortunately, nothing comes for free: the hidden cost behind ML performance stems from its high complexity in terms of operations to be computed and the involved amount of data. For this reasons, custom Artificial Intelligence hardware accelerators based on alternative ...
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Key words
Performance evaluation,Costs,Memory management,Machine learning,Complexity theory,Convolutional neural networks,Task analysis
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