Architectural Innovations for AI Chips, Testing, and High Performance Computing

Growth in the importance of artificial intelligence is coupled with AI-specialized chips and high-performance computing architectures, which pose challenges in testing and optimizing complex systems. The paper reviews the most recent architectural advancements in AI accelerators, testing techniques, and HPC systems. It will particularly point out research around AI processors in the design of GPU, ASIC, and FPGA and neuromorphic, design-for-test, built-in self-test, and online test techniques, trending HPC architectures, and considerations over performance, efficiency, and reliability. A proposed research direction is to leverage heterogeneous subsystem integration on-chip to harmonize the strengths of different computing paradigms. The paper focuses on new architectural designs for AI chips, with a special focus on the progress of neural network accelerators, memory hierarchies, and interconnect technologies. Such new innovations are going to improve the implementation of complex AI algorithms, with the effect of increased efficiency and decreased delay. The manuscript also discusses the integration of energy-efficient computing and heterogeneous integration as a remedy to the power-related problems emerging from the deployment of AI chips. The paper is focused on the progress of testing methodologies for AI chips. AI algorithms are complex and the application itself is very important; hence, a good testing framework is also a necessity. The paper discusses the use of fault injection, emulation, and simulation techniques for testing and verifying the AI-chip architectures to guarantee their reliability in practice. The paper conclusion will also hinge on how the AI chips will be integrated into the HPC systems. AI is going to increasingly intersect with the conventional high-performance computing tasks, thus placing great importance on the need for such integration.