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NITI: Training Integer Neural Networks Using Integer-only Arithmetic
[article]
2020
arXiv
pre-print
While integer arithmetic has been widely adopted for improved performance in deep quantized neural network inference, training remains a task primarily executed using floating point arithmetic. This is because both high dynamic range and numerical accuracy are central to the success of most modern training algorithms. However, due to its potential for computational, storage and energy advantages in hardware accelerators, neural network training methods that can be implemented with low precision
arXiv:2009.13108v1
fatcat:mvegf5mkbrabtkz4qfcbgdnpym