A novel multiplier-less convolution core for YOLO CNN ASIC implementation

The You Only Look Once (YOLO) algorithm has a good trade-off between accuracy and execution speed in object detection. The main bottleneck of execution speed in YOLO is the optimum implementation of the Convolutional Neural Network (CNN). Reducing convolution core resources to increase parallelism can significantly increase the execution speed of the Algorithm. A new ASIC Processing Element (PE) is presented in this paper to reduce power consumption and increase speed while utilizing fewer resources. A multiplier-less convolution core is proposed by replacing multipliers with multiplexer circuits and designing a 19-input adder. Reducing the weight word length to five bits and compensating for the accuracy with the new quantization, has made the accuracy of the new architecture competitive with previous works. Compared with the traditional convolutional core, the best-proposed core has been improved by 4.44X, 4.9X, and 32% in power consumption, area, and delay, respectively. Placing the proposed core in the PE, the power consumption, FPS, and accuracy were 1.76W, 55.8, and 78%, respectively. Although the proposed 3 × 3 convolution core was evaluated using YOLOv2 and YOLOv4-tiny, it is also applicable to YOLOv7 and YOLOv8.