Design of Sequential Wakeup Compute-In-Memory Controller Based on Convolutional Neural Network
With the development of artificial intelligence,the demand for intelligent image processing on edge devices has significantly increased.At present,edge devices mainly face issues such as limited energy and low throughput.For example,during reconnaissance,drones may need to perform complex terrain analysis,object recognition,and environmental monitoring functions according to their task requirements.These tasks often cause serious power consumption issues for the equipment in real-time,and seriously affect the flight time of drones.In response to these issues,this study proposes a design for a sequential wakeup Compute-In-Memory(CIM)controller based on a Convolutional Neural Network(CNN).This controller can perform forward inference for classified networks internally and wakeup corresponding edge devices based on the classification results.The analog part of the controller adopts a CIM calculation mode,whereas the digital part adopts a block processing method.During operation,idle modules can be dormant to reduce the overall power consumption of the system.In addition,the controller also integrates cascading interfaces,which can decompose complex tasks into multiple levels of subtasks and deploy them to cascading controllers,thereby achieving multi-level wakeup and giving the system the potential for early output.The results of an experiment using ResNet-14 as the neural network model and the CIFAR-10 dataset show that at a clock frequency of 10 MHz,the sequential wakeup CIM controller based on a CNN can achieve a detection frame rate of 60 frame/s and classification accuracy of 84.61％.The experimental results validate the feasibility and efficiency of this architecture in energy-constrained application scenarios.
万方数据
维普
