Deep Learning Training with Simulated Approximate Multipliers This paper presents by simulation how approximate multipliers can be utilized to enhance the training performance of convolutional neural networks (CNNs). Approximate multipliers have significantly better performance in terms of speed, power, and area compared to exact multipliers. However, approximate multipliers have an inaccuracy which is defined in terms of the Mean Relative Error (MRE). To assess the applicability of approximate multipliers in enhancing CNN training performance, a simulation for the impact of approximate multipliers error on CNN training is presented. The paper demonstrates that using approximate multipliers for CNN training can significantly enhance the performance in terms of speed, power, and area at the cost of a small negative impact on the achieved accuracy. Additionally, the paper proposes a hybrid training method which mitigates this negative impact on the accuracy. Using the proposed hybrid method, the training can start using approximate multipliers then switches to exact multipliers for the last few epochs. Using this method, the performance benefits of approximate multipliers in terms of speed, power, and area can be attained for a large portion of the training stage. On the other hand, the negative impact on the accuracy is diminished by using the exact multipliers for the last epochs of training.

A New Approximate Multiplier Design for Digital Signal Processing In this paper, a new approximate multiplier is proposed, which can decrease the multiplication complexity with the improved area and power performance by using OR and AND gates. To evaluate the efficiency of the proposed approximate multiplier, design parameters are compared with exact multiplier and recently proposed approximate designs. Experiment results reveal that the power consumption of the proposed approximate multiplier is only 32% of that in the exact multiplier. The proposed approximate multipliers are further evaluated in image sharpening and edge detection applications. The peak signal to noise ratio (PSNR) and structural similarity (SSIM) of our proposed approximate multipliers show that it can be used in image processing.