![]() ![]() Such virtualization methods could be used in teaching labs and for hardware evaluations.If you monitor closely what’s been downloaded and installed by Automatic Updates or Windows Update/Microsoft Update, you may have notice that there is an critical update named Windows Malicious Software Removal Tool with KB890830 label. Finally, we make the case for hardware virtualization by using the cloud and demonstrate how by means of a simple web browser we can program remote computing platforms connected to the cloud servers. With optimizations, the performance can surely be improved, this would be one of the key areas of future research work beyond this thesis. ![]() These performance improvements were obtained by using very basic and naive implementation of hardware accelerators generated at a high level of programming abstractions (OpenCL/Overlay APIs). We observe up to 10$\times$ improvement in timing performance in certain applications like 12-Tap FIR filtering when accelerated using hardware and almost 100$\times$ in certain applications like 2D Convolution. We experiment with simple and easy programming models like \ac or using Overlay APIs. This report presents an analysis of compute kernels (extracted from compute-intensive applications) and their implementation on multiple hardware accelerators, such as GPU, Altera OpenCL (AOCL) generated hardware accelerator and FPGA-based overlays. These architectures enable general purpose hardware accelerators, allowing hardware design at a higher level of abstraction. Coarse-grained FPGA overlays, such as VectorBlox MXP and DSP block based overlays, have been shown to be effective when paired with general purpose processors, offering software-like programmability, fast compilation, application portability and improved design productivity. Due to the complex process of FPGA-based accelerator design, the design productivity is a major issue, restricting the effective use of these accelerators to niche disciplines involving highly skilled hardware engineers. Research efforts have shown the strength of FPGA-based acceleration in a wide range of application domains where compute kernels can execute efficiently on an FPGA device. ![]()
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