16-bit log-based training achieves classification accuracy within approximately 1% of floating-point baselines on common datasets by replacing multiplications with approximate log-domain additions.
Neural Network Training with Approximate Logarithmic Computations
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abstract
The high computational complexity associated with training deep neural networks limits online and real-time training on edge devices. This paper proposed an end-to-end training and inference scheme that eliminates multiplications by approximate operations in the log-domain which has the potential to significantly reduce implementation complexity. We implement the entire training procedure in the log-domain, with fixed-point data representations. This training procedure is inspired by hardware-friendly approximations of log-domain addition which are based on look-up tables and bit-shifts. We show that our 16-bit log-based training can achieve classification accuracy within approximately 1% of the equivalent floating-point baselines for a number of commonly used datasets.
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Neural Network Training with Approximate Logarithmic Computations
16-bit log-based training achieves classification accuracy within approximately 1% of floating-point baselines on common datasets by replacing multiplications with approximate log-domain additions.