CNS Core: Small: AccelRITE: Accelerating ReInforcemenT Learning based AI at the Edge Using FPGAs

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Artificial Intelligence (AI) has led to significant progress in several domains such as self-driving cars and robotics. Reinforcement Learning (RL) is a class of AI that includes algorithms that enable machines to teach themselves optimal decision making. However, RL algorithms are complex and time-consuming, which render them unsuitable for applications that require fast response. Heterogeneous platforms, which couple a Central Processing Unit (CPU) with an integrated circuit that can be configured - Field Programmable Gate Arrays (FPGA) are promising candidates for implementing fast algorithms due to their capabilities. The project will develop fast implementations of RL algorithms targeting such platforms. The intellectual merits of the project include the research and development of innovative optimizations that exploit the heterogeneity of the emerging class of FPGA devices and address challenges such as conflicts in parallel accesses to shared objects, irregular memory accesses, and overheads in fine grained acceleration. The project will develop parameterized performance models for key AI kernels – Stochastic Gradient Descent (SGD), conjugate gradient, parallel hash tables, and neural networks, to enable energy-performance trade-off analysis. The proposed project will develop a novel spatiotemporal constraint graph-based design space exploration technique to accelerate RL algorithms by taking a holistic view of the algorithm.The broader impact of the project is in efficient use of heterogeneous architectures consisting of CPUs and FPGAs coupled with cache coherent memory for accelerating AI for edge computing. Successful completion of this project will lead to significant increase in the complexity of AI applications that can be deployed in real world environments. This will lead to a dramatic improvement in the capabilities of AI enabled devices such as self-driving cars, robotics, and wearable healthcare devices. The project will also constitute materials appropriate for inclusion in graduate and undergraduate courses.All software developed in the project will be posted on github at: https://github.com/pgroupATusc. Software releases will be maintained for a period of not less than 3 years after the conclusion of the grant.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.