Training deep neural networks(DNNs)requires a significant amount of time and resources to obtain acceptable results,which severely limits its deployment in resource-limited platforms.This paper proposes DarkFPGA,a nov...Training deep neural networks(DNNs)requires a significant amount of time and resources to obtain acceptable results,which severely limits its deployment in resource-limited platforms.This paper proposes DarkFPGA,a novel customizable framework to efficiently accelerate the entire DNN training on a single FPGA platform.First,we explore batch-level parallelism to enable efficient FPGA-based DNN training.Second,we devise a novel hardware architecture optimised by a batch-oriented data pattern and tiling techniques to effectively exploit parallelism.Moreover,an analytical model is developed to determine the optimal design parameters for the DarkFPGA accelerator with respect to a specific network specification and FPGA resource constraints.Our results show that the accelerator is able to perform about 10 times faster than CPU training and about a third of the energy consumption than GPU training using 8-bit integers for training VGG-like networks on the CIFAR dataset for the Maxeler MAX5 platform.展开更多
针对分布式存储中数据外包的安全性、动态更新与批量验证问题,提出一种支持动态更新的可验证不可压缩编码方案。该方案基于判定复合剩余(Decisional Composite Residuosity,DCR)假设构建满射损失函数,实现抗压缩攻击的不可压缩编码,并...针对分布式存储中数据外包的安全性、动态更新与批量验证问题,提出一种支持动态更新的可验证不可压缩编码方案。该方案基于判定复合剩余(Decisional Composite Residuosity,DCR)假设构建满射损失函数,实现抗压缩攻击的不可压缩编码,并采用可组合结构支持多副本存储。通过设计轻量索引结构的块索引链(Block Index Chain,BIC)与空闲节点索引池(Free Node Index Pool,FNIP),实现块级动态更新,同时利用BLS(Boneh-Lynn-Shacham)签名算法实现聚合批量验证。安全性分析表明,所提方案在随机预言机模型下具备可组合的不可压缩性与结构完整性保障。性能分析结果表明,与现有方案相比,所提方案在单副本与多副本场景下编码耗时更低,其中单副本场景平均性能提升约24.8%,多副本场景在副本数较高时性能提升可达70%以上,整体效率优势显著。展开更多
摘要Training deep neural networks(DNNs)requires a significant amount of time and resources to obtain acceptable results,which severely limits its deployment in resource-limited platforms.This paper proposes DarkFPGA,a novel customizable framework to efficiently accelerate the entire DNN training on a single FPGA platform.First,we explore batch-level parallelism to enable efficient FPGA-based DNN training.Second,we devise a novel hardware architecture optimised by a batch-oriented data pattern and tiling techniques to effectively exploit parallelism.Moreover,an analytical model is developed to determine the optimal design parameters for the DarkFPGA accelerator with respect to a specific network specification and FPGA resource constraints.Our results show that the accelerator is able to perform about 10 times faster than CPU training and about a third of the energy consumption than GPU training using 8-bit integers for training VGG-like networks on the CIFAR dataset for the Maxeler MAX5 platform.
摘要针对分布式存储中数据外包的安全性、动态更新与批量验证问题,提出一种支持动态更新的可验证不可压缩编码方案。该方案基于判定复合剩余(Decisional Composite Residuosity,DCR)假设构建满射损失函数,实现抗压缩攻击的不可压缩编码,并采用可组合结构支持多副本存储。通过设计轻量索引结构的块索引链(Block Index Chain,BIC)与空闲节点索引池(Free Node Index Pool,FNIP),实现块级动态更新,同时利用BLS(Boneh-Lynn-Shacham)签名算法实现聚合批量验证。安全性分析表明,所提方案在随机预言机模型下具备可组合的不可压缩性与结构完整性保障。性能分析结果表明,与现有方案相比,所提方案在单副本与多副本场景下编码耗时更低,其中单副本场景平均性能提升约24.8%,多副本场景在副本数较高时性能提升可达70%以上,整体效率优势显著。