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
Existing Side-Channel Attacks （SCAs） have several limitations and, rather than to be real attack methods, can only be considered to be security evaluation methods. Their limitations are mainly related to the samplin...Existing Side-Channel Attacks （SCAs） have several limitations and, rather than to be real attack methods, can only be considered to be security evaluation methods. Their limitations are mainly related to the sampling conditions, such as the trigger signal embedded in the source code of the encryption device, and the acquisition device that serves as the encryption-device controller. Apart from it being very difficult for an attacker to add a trigger into the original design before making an attack or to control the encryption device, there is a big gap in the capacity of existing SCAs to pose real threats to cipher devices. In this paper, we propose a new method, the sliding window SCA （SW-SCA）, which can be applied in scenarios in which the acquisition device is independent of the encryption device and for which the encryption source code requires no trigger signal or modification. First, we describe the main issues in existing SCAs, then we theoretically analyze the effectiveness and complexity of our proposed SW-SCA --a method that can incorporate a sliding-window mechanism into almost all of the existing non-profiled SCAs. The experimental results for both simulated and physical traces verify the effectiveness of the SW-SCA and the appropriateness of its theoretical complexity.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upported by the National Natural Science Foundation of China (No. 61472292)the Technological Innovation of Hubei Province (No. 2018AAA046)the Key Technology Research of New-Generation HighSpeed and High-Level Security Chip for Smart Grid (No. 526816160015)
文摘Existing Side-Channel Attacks （SCAs） have several limitations and, rather than to be real attack methods, can only be considered to be security evaluation methods. Their limitations are mainly related to the sampling conditions, such as the trigger signal embedded in the source code of the encryption device, and the acquisition device that serves as the encryption-device controller. Apart from it being very difficult for an attacker to add a trigger into the original design before making an attack or to control the encryption device, there is a big gap in the capacity of existing SCAs to pose real threats to cipher devices. In this paper, we propose a new method, the sliding window SCA （SW-SCA）, which can be applied in scenarios in which the acquisition device is independent of the encryption device and for which the encryption source code requires no trigger signal or modification. First, we describe the main issues in existing SCAs, then we theoretically analyze the effectiveness and complexity of our proposed SW-SCA --a method that can incorporate a sliding-window mechanism into almost all of the existing non-profiled SCAs. The experimental results for both simulated and physical traces verify the effectiveness of the SW-SCA and the appropriateness of its theoretical complexity.
