A Cost-Efficient Iterative Truncated Logarithmic Multiplication for Convolutional Neural Networks

This paper proposes a cost-efficient approximate logarithmic multiplication for convolutional neural networks (CNNs), where two truncated logarithmic multipliers are connected for error correction. The proposed iterative logarithmic multiplication achieves low and unbiased average error while the hardware cost is significantly reduced by utilizing the truncated Mitchell multiplier and approximating error terms from the first stage. The proposed design has error characteristics that are suitable for neural network inferences, and the experiments on contemporary CNNs show that the proposed multiplier does not cause significant degradation on accuracy compared to exact multiplication.