Low-power implementation of Mitchell's approximate logarithmic multiplication for convolutional neural networks

This paper proposes a low-power implementation of the approximate logarithmic multiplier to improve the power consumption of convolutional neural networks for image classification, taking advantage of its intrinsic tolerance to error. The approximate logarithmic multiplier converts multiplications to additions by taking approximate logarithm and achieves significant improvement in power and area while having low worst-case error, which makes it suitable for neural network computation. Our proposed design shows a significant improvement in terms of power and area over the previous work that applied logarithmic multiplication to neural networks, reducing power up to 76.6% compared to exact fixed-point multiplication, while maintaining comparable prediction accuracy in convolutional neural networks for MNIST and CIFAR10 datasets.