Low-Power Implementation Techniques for Convolutional Neural Networks using Precise and Active Skipping Methods

Reducing power consumption is crucial for edge devices using convolutional neural network (CNN). The zero-skipping approach for CNNs is a processing technique widely known for its relatively low power consumption and high speed. This approach stops multiplication and accumulation (MAC) when the multiplication results of the input data and weight are zero. However, this technique requires large logic circuits with around 5% overhead, and the average rate of MAC stopping is approximately 30%. In this paper, we propose a precise zero-skipping method that uses input data and simple logic circuits to stop multipliers and accumulators precisely. We also propose an active data-skipping method to further reduce power consumption by slightly degrading recognition accuracy. In this method, each multiplier and accumulator are stopped by using small values (e.g., 1, 2) as input. We implemented single shot multi-box detector 500 (SSD500) network model on a Xilinx ZU9 and applied our proposed techniques. We verified that operations were stopped at a rate of 49.1%, recognition accuracy was degraded by 0.29%, power consumption was reduced from 9.2 to 4.4 W (−52.3%), and circuit overhead was reduced from 5.1 to 2.7% (−45.9%). The proposed techniques were determined to be effective for lowering the power consumption of CNN-based edge devices such as FPGA. key words: convolutional neural network (CNN), SSD500 network, deep neural network (DNN) implementation, low power consumption, embedded AI technique