Design of ASIC accelerators for AI applications

This paper explores the advantages, architectural advancements, and challenges of ASIC (Application-Specific Integrated Circuits) accelerators in AI applications. Firstly, it provides an in-depth analysis of ASICs compared to different architectures. Due to their specialized design, ASICs exhibit significant advantages in performance and energy efficiency. For instance, architectures like DianNao and DaDianNao excel in handling large-scale neural networks by optimizing data locality and employing tiling techniques. In contrast, GPUs, while excellent in parallel processing, consume more power due to their general-purpose nature. FPGAs strike a balance between computational speed and energy consumption. The paper then delves into the Google TPU as a pivotal case study of ASIC accelerator design architecture for AI applications. The TPU's matrix multiplication unit and high-bandwidth memory optimizations enable high computational throughput and energy efficiency in neural network computations. TPUv4i further enhances performance by incorporating more matrix multiplication units and leveraging compiler optimizations, while also reducing thermal design power for global deployment. Innovations such as Optical Circuit Switching (OCS) and SparseCore integration significantly improve TPUv4's system scale, availability, and performance. Finally, the paper addresses the challenges of ASIC accelerators in AI applications. In high-radiation environments, edge-AI ASICs require radiation-hardened features to ensure reliable performance. In summary, ASICs provide unparalleled performance and energy efficiency owing to their specialized design, but they encounter challenges in high-radiation environments and thermal management. Through ongoing architectural innovations and technological optimizations, ASICs are well-positioned to play a pivotal role in the future of AI hardware design.