Deep neural networks are commonly developed and trained in 32-bit floating point format. Significant gains in performance and energy efficiency could be realized by training and inference in numerical formats optimized for deep learning. Despite advances in limited precision inference in recent years, training of neural networks in low bit-width remains a challenging problem...
Authors
Naveen Rao
Vice President & General Manager, Artificial Intelligence Products Group
William H. Constable
Scott Gray
Stewart Hall
Luke Hornof
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