12/18/2022 0 Comments Cpu speed accelerator 6.2 serial![]() ![]() ACSA is able to adopt crypto mode adaptively and dynamically according to the request character and system load. We not only proposed optimal strategies such as data aggregation to advance the contribution with hardware crypto engines, but also presented an Adaptive Crypto System based on Accelerators (ACSA) with software and hardware codesign. Therefore, we proposed the research to take full advantages of both accelerators and CPUs for security HTTP accesses in big data. Actually, for some application scenarios, the performance improvement may not be comparable with AES-NI, due to the induced invocation cost for hardware engines. Hardly of them presented how to utilize them efficiently. Although there are lots of excellent works with the objective of SSL/TLS hardware acceleration, they focus on the dedicated hardware design of accelerators. These expensive computations through software implementations may not be able to compete with the increasing need for speed and secure connection. Although OpenSSL could provide a freely available implementation of the SSL/TLS protocol, the crypto functions, such as symmetric key ciphers, are extremely compute-intensive operations. The SSL/TLS protocol has been widely adopted as one of the effective solutions for sensitive access. He is IEEE senior member since 2006.Along with the explosive growth of network data, security is becoming increasingly important for web transactions. His research activity is supported by more than 150 publications on international conferences, more than 10 books chapters, and more than 30 scientific journals. He serves as technical reviewer and committee member of multiple journals and international conferences. ![]() He has participated in more than 18 national and international research projects, from 4 G chips development to next-generation tracking devices and architectures for IoT, HPC and space. In 2009, he was responsible for the creation of the spinoff Nsilition from a project funded by the Walloon Region. He was team manager at CoWare, today Synopsys (1999–2004, Belgium). He was invited professor at the UCC (2011, Cordoba, Argentina), at the UFPE (2004, Pernambuco, Brazil) and at the UFRN (1998 & 2017, Rio Grande do Norte, Brazil). diploma at the UFRN/COPPE (1993, Rio de Janeiro, Brazil), and the electrical-electronics engineering diploma at the UNC (1989, Cordoba, Argentina). degree at the INPG (1998, Grenoble, France), the M. Navigate Left Previous article in issueĬarlos Valderrama is, since 2004, Director of the Electronics and Microelectronics Department at the Polytechnic Faculty of the University of Mons in Belgium and former member of the Numediart and InforTech institutes.In this context, we will examine the frameworks used in these studies, which will allow testing a lot of topologies to finally arrive at the best implementation alternatives in terms of performance and energy efficiency. We will analyze and compare the design requirements and features of existing topologies to finally propose development strategies and implementation architectures for better use of FPGA-based deep learning topologies. Through this paper, we briefly review recent work related to the implementation of deep learning algorithms in FPGAs. Although limited in size and resources, several approaches have showed that FPGAs provide a good starting point for the development of future deep learning implementation architectures. ![]() Consequently, in the context of real time hardware systems, it is crucial to find the right trade-off between performance, energy efficiency, fast development, and cost. However, inspired by the structure and function of ANNs, large-scale deep learning topologies require a considerable amount of parallel processing, memory resources, high throughput and significant processing power. Deep learning, the fastest growing segment of Artificial Neural Network (ANN), has led to the emergence of many machine learning applications and their implementation across multiple platforms such as CPUs, GPUs and reconfigurable hardware ( Field-Programmable Gate Arrays or FPGAs). ![]()
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