With the rapid development and popularization of artificial intelligence technology,convolutional neural network(CNN)is applied in many fields,and begins to replace most traditional algorithms and gradually deploys to...With the rapid development and popularization of artificial intelligence technology,convolutional neural network(CNN)is applied in many fields,and begins to replace most traditional algorithms and gradually deploys to terminal devices.However,the huge data movement and computational complexity of CNN bring huge power consumption and performance challenges to the hardware,which hinders the application of CNN in embedded devices such as smartphones and smart cars.This paper implements a convolutional neural network accelerator based on Winograd convolution algorithm on field-programmable gate array(FPGA).Firstly,a convolution kernel decomposition method for Winograd convolution is proposed.The convolution kernel larger than 3×3 is divided into multiple 3×3 convolution kernels for convolution operation,and the unsynchronized long convolution operation is processed.Then,we design Winograd convolution array and use configurable multiplier to flexibly realize multiplication for data with different accuracy.Experimental results on VGG16 and AlexNet network show that our accelerator has the most energy efficient and 101 times that of the CPU,5.8 times that of the GPU.At the same time,it has higher energy efficiency than other convolutional neural network accelerators.展开更多
To solve the hardware deployment problem caused by the vast demanding computational complexity of convolutional layers and limited hardware resources for the hardware network inference,a look-up table(LUT)-based convo...To solve the hardware deployment problem caused by the vast demanding computational complexity of convolutional layers and limited hardware resources for the hardware network inference,a look-up table(LUT)-based convolution architecture built on a field-programmable gate array using integer multipliers and addition trees is used.With the help of the Winograd algorithm,the optimization of convolution and multiplication is realized to reduce the computational complexity.The LUT-based operator is further optimized to construct a processing unit(PE).Simultaneously optimized storage streams improve memory access efficiency and solve bandwidth constraints.The data toggle rate is reduced to optimize power consumption.The experimental results show that the use of the Winograd algorithm to build basic processing units can significantly reduce the number of multipliers and achieve hardware deployment acceleration,while the time-division multiplexing of processing units improves resource utilization.Under this experimental condition,compared with the traditional convolution method,the architecture optimizes computing resources by 2.25 times and improves the peak throughput by 19.3 times.The LUT-based Winograd accelerator can effectively solve the deployment problem caused by limited hardware resources.展开更多
基金supported by the Project of the State Grid Corporation of China in 2022(No.5700-201941501A-0-0-00)the National Natural Science Foundation of China(No.U21B2031).
文摘With the rapid development and popularization of artificial intelligence technology,convolutional neural network(CNN)is applied in many fields,and begins to replace most traditional algorithms and gradually deploys to terminal devices.However,the huge data movement and computational complexity of CNN bring huge power consumption and performance challenges to the hardware,which hinders the application of CNN in embedded devices such as smartphones and smart cars.This paper implements a convolutional neural network accelerator based on Winograd convolution algorithm on field-programmable gate array(FPGA).Firstly,a convolution kernel decomposition method for Winograd convolution is proposed.The convolution kernel larger than 3×3 is divided into multiple 3×3 convolution kernels for convolution operation,and the unsynchronized long convolution operation is processed.Then,we design Winograd convolution array and use configurable multiplier to flexibly realize multiplication for data with different accuracy.Experimental results on VGG16 and AlexNet network show that our accelerator has the most energy efficient and 101 times that of the CPU,5.8 times that of the GPU.At the same time,it has higher energy efficiency than other convolutional neural network accelerators.
基金The Academic Colleges and Universities Innovation Program 2.0(No.BP0719013)。
文摘To solve the hardware deployment problem caused by the vast demanding computational complexity of convolutional layers and limited hardware resources for the hardware network inference,a look-up table(LUT)-based convolution architecture built on a field-programmable gate array using integer multipliers and addition trees is used.With the help of the Winograd algorithm,the optimization of convolution and multiplication is realized to reduce the computational complexity.The LUT-based operator is further optimized to construct a processing unit(PE).Simultaneously optimized storage streams improve memory access efficiency and solve bandwidth constraints.The data toggle rate is reduced to optimize power consumption.The experimental results show that the use of the Winograd algorithm to build basic processing units can significantly reduce the number of multipliers and achieve hardware deployment acceleration,while the time-division multiplexing of processing units improves resource utilization.Under this experimental condition,compared with the traditional convolution method,the architecture optimizes computing resources by 2.25 times and improves the peak throughput by 19.3 times.The LUT-based Winograd accelerator can effectively solve the deployment problem caused by limited hardware resources.
