Constraint-aware configurable system-on-chip design for embedded computing

Field Programmable Gate Arrays (FPGAs) are rapidly becoming a popular alternative to ASICs as they continue to increase in capacity, functionality and performance. At the same time, FPGA developers are faced with the challenges of meeting increasingly aggressive design constraints such as power, delay and area costs without violating shorter Time-to-Market (TTM) pressures and lower Non-Recurring Engineering (NRE) costs for embedded systems development. In this research, efficient techniques have been proposed for processor subsetting and customization as well as the rapid generation of application-specific hardware accelerators in order to meet the design constraints of configurable System-on-Chip (SoC) platforms. A processor-agnostic technique has been devised for sub-setting soft-core processors by relying on LLVM compiler generated front-end application output. The proposed approach has resulted in a systematic method for the application-aware sub-setting of the micro-architecture subsystems such as hardware multipliers and floating point units of a soft-core processor. Evaluations based on widely used benchmarks show that the proposed method can be deployed to reliably subset soft core processors at high-speed without compromising compute performance. A technique for the architecture-aware enumeration of custom instructions has been proposed next to identify area-efficient custom instructions by employing FPGA resource-aware pruning of the search space. Experimental results based on applications from widely-used benchmark suites confirm that deploying custom instructions identified in this way can improve compute performance by up to 65%. The instruction level parallelism (ILP) has also been exploited to further improve the compute performance by identifying profitable coarsegrained custom instructions. It has been demonstrated that the custom instructions using the proposed method can accelerate computations by up to 39% when compared to a base processor only implementation. Unlike traditional custom instruction generation methods that are incapable of incorporating memory-dependent basic blocks, a novel technique for accelerating memory-dependent basic blocks has been proposed. A detailed data dependency analysis based on pre-defined memory allocation in an application has been developed to guarantee the identification of