SHF:Small:Design Validation Using Multiple Concurrent Abstract Models and GPGPUs

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A critical bottleneck in the design of large, complex hardware systems is verification and validation, whose goal is to check if the implementation meets the specifications. An ill-verified design compromises not only reliability, but also security of the hardware. Therefore, having a sound and effective verification framework plays a critical role in designing today's large scale circuits. However, the exponential growth in circuit complexity over the past few decades has made verification and validation an extremely daunting task. Hence, in many projects the level of effort needed to verify the correctness of systems often far exceeds efforts spent on design. Thus, much-needed verification and validation breakthroughs hold the key to ease this mounting challenge. The objectives of this project is to address this need via four coherent tasks: multiple abstractions for extracting various core functional behaviors and diversifying search perspectives; swarm-aggregate learning of branching behavior and necessary loop repetitions; combined particle swarm optimization with ACO in generating long sequences; and GPGPUs for enhancing performance and scalability. Together, these tasks elicit the collective power of diverse perspectives, thus aiming to advance the knowledge of verification. The proposed approach is flexible and is not restricted by the inherent depth limitation imposed by deterministic methods. Furthermore, with swarm-aggregate learning applied to GPGPUs, the computational cost can be significantly reduced. It is expected that the synergy from simulation, swarm intelligence, multiple concurrent abstractions, and GPGPUs will bring out the best from each domain to achieve a common goal.Making significant strides in the field of verification and validation will not only reduce time to market products, but also will increase national competitiveness both from the technical and economic standpoint using design optimizations previously deemed unattainable. Through the Multicultural Academic Opportunities Program (MAOP) at Virginia Tech the PI will advise a diverse team of graduate students, including both women and minority students under-represented in engineering. The project also plans continued expansion of downloadable resources (tools, benchmarks, etc.), benefiting both industry and academia.