Abstract-Field-programmable gate arrays (FPGAs) are increasingly used to implement embedded digital systems, however, the hardware design necessary to do so is time-consuming and tedious. The amount of hardware design can be reduced by employing a microprocessor for less-critical computation in the system. Often this microprocessor is implemented using the FPGA reprogrammable fabric as a soft processor which presently have simple architectures and moderate performance. Our goal is to scale the performance of existing soft processors hence expanding their suitability to more critical computation. To this end we propose extending soft processors with vector extensions to exploit the abundant data parallelism found in many embedded kernels. Such a soft vector processor can execute these kernels much faster than a single-core hence reducing the need for hardware implementations. We observe this improved execution speed through experimentation with vector extended soft processor architecture (VESPA) which is designed, implemented, and evaluated on real FPGA hardware. VESPA is shown to effectively scale performance up to 32 lanes, while providing substantial architectural flexibility to create a fine-grained design space. With these characteristics, and portability across FPGA devices, soft vector processors can provide exact-fit architectures which can efficiently and more easily implement data parallel workloads over custom FPGA hardware design.Index Terms-Customization, design space exploration, field-programmable gate array (FPGA)-based soft-core processors, processor generator.
As more embedded systems are built using FPGA platforms, there is an increasing need to support processors in FPGAs. One option is the soft processor, a processor implemented in the reconfigurable logic of the FPGA. Commercial soft processors have been widely deployed, and hence we are motivated to understand their microarchitecture. We must re-evaluate microarchitecture in the soft processor context because an FPGA platform is significantly different than an ASIC platform. This dissertation presents an infrastructure for rapidly generating RTL models of soft processors, as well as a methodology for measuring their area, performance, and power. Using the automatically-generated soft processors we explore many interesting microarchitectural axes in the trade-off space. We also compare our designs to Altera's Nios II commercial soft processors and find that our automatically generated designs span the design space while remaining very competitive.ii
A key advantage of soft processors (processors built on an FPGA programmable fabric) over hard processors is that they can be customized to suit an application program's specific software. This notion has been exploited in the past principally through the use of application-specific instructions. While commercial soft processors are now widely deployed, they are available in only a few microarchitectural variations. In this work we explore the advantage of tuning the processor's microarchitecture to specific software applications, and show that there are significant advantages in doing so.Using an infrastructure for automatically generating soft processors that span the area/speed design space (while remaining competitive with Altera's Nios II variations), we explore the impact of tuning several aspects of microarchitecture including: (i) hardware vs software multiplication support; (ii) shifter implementation; and (iii) pipeline depth, organization, and forwarding. We find that the processor design that is fastest overall (on average across our embedded benchmark applications) is often also the fastest design for an individual application. However, in terms of area efficiency (i.e., performance-per-area), we demonstrate that a tuned microarchitecture can offer up to 30% improvement for three of the benchmarks and on average 11.4% improvement over the fastest-on-average design. We also show that our benchmark applications use only 50% of the available instructions on average, and that a processor customized to support only that subset of the ISA for a specific application can on average offer 25% savings in both area and energy. Finally, when both techniques for customization are combined we obtain an average improvement in performance-per-area of 25%.
Abstract-As embedded systems designers increasingly use field-programmable gate arrays (FPGAs) while pursuing single-chip designs, they are motivated to have their designs also include soft processors, processors built using FPGA programmable logic. In this paper, we provide: 1) an exploration of the microarchitectural tradeoffs for soft processors and 2) a set of customization techniques that capitalizes on these tradeoffs to improve the efficiency of soft processors for specific applications. Using our infrastructure for automatically generating soft-processor implementations (which span a large area/speed design space while remaining competitive with Altera's Nios II variations), we quantify tradeoffs within soft-processor microarchitecture and explore the impact of tuning the microarchitecture to the application. In addition, we apply a technique of subsetting the instruction set to use only the portion utilized by the application. Through these two techniques, we can improve the performance-per-area of a soft processor for a specific application by an average of 25%.Index Terms-Customization, design space exploration, field--programmable gate array (FPGA)-based soft-core processors, processor generator.
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