当前,数据已成为关键战略资源,数据挖掘和分析技术在各行业发挥着重要作用,但也存在着数据泄露的风险。安全函数计算(Secure Function Evaluation,SFE)可以在保证数据安全的前提下完成任意函数的计算。Yao协议是一种用于实现安全函数计...当前,数据已成为关键战略资源,数据挖掘和分析技术在各行业发挥着重要作用,但也存在着数据泄露的风险。安全函数计算(Secure Function Evaluation,SFE)可以在保证数据安全的前提下完成任意函数的计算。Yao协议是一种用于实现安全函数计算的协议,该协议在混淆电路(Garbled Circuit,GC)生成和计算阶段含有大量加解密计算操作,且在不经意传输(Oblivious Transfer,OT)阶段具有较高的计算开销,难以满足复杂的现实应用需求。针对Yao协议的效率问题,基于现场可编程门阵列(Field Programmable Gate Array,FPGA)的异构计算对Yao协议进行加速,并结合提出的轻量级代理不经意传输协议,最终设计出轻量级异构安全计算加速框架。该方案中,混淆电路生成方和代理计算方都实现了CPU-FPGA异构计算架构。该架构借助CPU擅长处理控制流的优势和FPGA的并行处理优势对混淆电路生成阶段和计算阶段进行加速,提高了生成混淆电路和计算混淆电路的效率,减轻了计算压力。另外,相比于通过非对称密码算法实现的不经意传输协议,在轻量级代理不经意传输协议中,混淆电路生成方和代理计算方只需执行对称操作,代理计算方即可获取用户输入对应的生成方持有的随机数。该轻量级代理不经意传输协议减轻了用户和服务器在不经意传输阶段的计算压力。实验证明,在局域网环境下,与Yao协议的软件实现(TinyGarble框架)相比,该方案的计算效率至少提高了128倍。展开更多
A quantum BP neural networks model with learning algorithm is proposed. First, based on the universality of single qubit rotation gate and two-qubit controlled-NOT gate, a quantum neuron model is constructed, which is...A quantum BP neural networks model with learning algorithm is proposed. First, based on the universality of single qubit rotation gate and two-qubit controlled-NOT gate, a quantum neuron model is constructed, which is composed of input, phase rotation, aggregation, reversal rotation and output. In this model, the input is described by qubits, and the output is given by the probability of the state in which （1） is observed. The phase rotation and the reversal rotation are performed by the universal quantum gates. Secondly, the quantum BP neural networks model is constructed, in which the output layer and the hide layer are quantum neurons. With the application of the gradient descent algorithm, a learning algorithm of the model is proposed, and the continuity of the model is proved. It is shown that this model and algorithm are superior to the conventional BP networks in three aspects： convergence speed, convergence rate and robustness, by two application examples of pattern recognition and function approximation.展开更多
该文基于现场可编程门阵列(field-programmable gate array,FPGA),为永磁同步电机驱动提出一种扩张控制集模型预测电流控制策略(model predictive current control,MPCC)。由于在每个控制周期内只有8个基本电压矢量可供选择,传统有限控...该文基于现场可编程门阵列(field-programmable gate array,FPGA),为永磁同步电机驱动提出一种扩张控制集模型预测电流控制策略(model predictive current control,MPCC)。由于在每个控制周期内只有8个基本电压矢量可供选择,传统有限控制集模型预测电流控制(finite control set MPCC,FCS-MPCC)稳态性能较低。为此,文中采用具有818个可选矢量的ECS来实现更精细的电压输出。为减轻因电压矢量大幅增加而带来的计算负担,设计一种简化的最优矢量搜索策略,且可推广用于其他多目标成本函数。基于算法固有并行性,将所提ECS-MPCC方法在FPGA中进行实现,使电流环总控制时间缩短至0.59μs,从而可以消除计算延迟,提高电流环动态性能。最后,通过仿真和实验,验证所提ECS-MPCC策略的有效性。实验结果表明,与传统FCS-MPCC相比,ECS-MPCC的相电流总谐波失真降低77%。展开更多
基金the National Natural Science Foundation of China (50138010)
文摘A quantum BP neural networks model with learning algorithm is proposed. First, based on the universality of single qubit rotation gate and two-qubit controlled-NOT gate, a quantum neuron model is constructed, which is composed of input, phase rotation, aggregation, reversal rotation and output. In this model, the input is described by qubits, and the output is given by the probability of the state in which （1） is observed. The phase rotation and the reversal rotation are performed by the universal quantum gates. Secondly, the quantum BP neural networks model is constructed, in which the output layer and the hide layer are quantum neurons. With the application of the gradient descent algorithm, a learning algorithm of the model is proposed, and the continuity of the model is proved. It is shown that this model and algorithm are superior to the conventional BP networks in three aspects： convergence speed, convergence rate and robustness, by two application examples of pattern recognition and function approximation.
文摘该文基于现场可编程门阵列(field-programmable gate array,FPGA),为永磁同步电机驱动提出一种扩张控制集模型预测电流控制策略(model predictive current control,MPCC)。由于在每个控制周期内只有8个基本电压矢量可供选择,传统有限控制集模型预测电流控制(finite control set MPCC,FCS-MPCC)稳态性能较低。为此,文中采用具有818个可选矢量的ECS来实现更精细的电压输出。为减轻因电压矢量大幅增加而带来的计算负担,设计一种简化的最优矢量搜索策略,且可推广用于其他多目标成本函数。基于算法固有并行性,将所提ECS-MPCC方法在FPGA中进行实现,使电流环总控制时间缩短至0.59μs,从而可以消除计算延迟,提高电流环动态性能。最后,通过仿真和实验,验证所提ECS-MPCC策略的有效性。实验结果表明,与传统FCS-MPCC相比,ECS-MPCC的相电流总谐波失真降低77%。
文摘现有的光流估计网络为了获得更高的精度,往往使用相关性成本量和门控循环单元(gate recurrent unit,GRU)来进行迭代优化,但是这样会导致计算量大并限制了在边缘设备上的部署性能。为了实现更轻量的光流估计方法,本文提出局部约束与局部扩张模块(local constraint and local dilation module,LC-LD module),通过结合卷积和一次轴注意力来替代自注意力,以较低的计算量对每个匹配特征点周边区域内不同重要程度的关注,生成更准确的相关性成本量,进而降低迭代次数,达到更轻量化的目的。其次,提出了混洗凸优化上采样,通过将分组卷积、混洗操作与凸优化上采样相结合,在实现其参数数量降低的同时进一步提高精度。实验结果证明了该方法在保证高精度的同时,运行效率显著提升,具有较高的应用前景。
