Increasingly, soft processors are being considered for use within FPGA-based reliable computing systems. In an environment in which radiation is a concern, such as space, the logic and routing (configuration memory) of soft processors are sensitive to radiation effects, including single event upsets (SEUs). Thus, effective tools are needed to evaluate and estimate how sensitive the configuration memories of soft processors are in high-radiation environments. A high-speed FPGA fault injection system and methodology were created using the Xilinx Radiation Test Consortium's (XRTC's) Virtex-5 radiation test hardware to conduct exhaustive tests of the SEU sensitivity of a design within an FPGA's configuration memory. This tool was used to show that the sensitivity of the configuration memory of a soft processor depends on several variables, including its microarchitecture, its customizations and features, and the software instructions that are executed. The fault injection experiments described in this thesis were performed on five different soft processors, i.e., MicroBlaze, LEON3, Arm Cortex-M0 DesignStart, OpenRISC 1200, and PicoBlaze. Emphasis was placed on characterizing the sensitivity of the MicroBlaze soft processor and the dependence of the sensitivity on various modifications. Seven benchmarks were executed through the various experiments and used to determine the SEU sensitivity of the soft processor's configuration memory to the instructions that were executed. In this thesis, a wide variety of soft processor fault injection results are presented to show the differences in sensitivity between multiple soft processors and the software they run.

Soft multiprocessor systems exploit the plentiful computational resources available in field programmable devices. By virtue of their adaptability and ability to support coarse grained parallelism, they serve as excellent platforms for rapid prototyping and design space exploration of embedded multiprocessor applications. As complex applications emerge, careful mapping, processor and interconnect customization are critical to the overall performance of the multiprocessor system. In this thesis, we have developed an automated scalable framework to efficiently map applications written in a high-level programmer-friendly language to customizable soft-cores. The framework allows the user to specify the application in a high-level language called Streamit. After an initial analysis of the application, a soft multiprocessor system is generated automatically using a set of customizable SPREE processors which communicate with each other over point-to-point FIFO connections. Several micro-architectural features of the processors are then automatically customized on a per-application basis to improve system area, performance and power consumption. The efficiency and scalability of this approach has been validated using a diverse set of eight audio, video and signal processing benchmarks on soft multiprocessor systems consisting of one to sixteen processors. Results show that generated soft multiprocessor systems consisting of sixteen processors can offer up to 6x speedup over a conventional single processor system. Our experiments with soft multiprocessor interconnection networks show that point-to-point topologies perform approximately 2x better than mesh topologies. Finally, we demonstrate that application-specific customizations on the instruction set, memory size, and inter-processor buffer size can improve the area and performance of the generated soft multiprocessor systems. The developed framework facilitates rapid design space exploration of soft multiprocessors.