High-Bandwidth NoC Design for AI Accelerator Platforms

Abstract

The rapid development of artificial intelligence (AI), machine learning, and data-centric computing has significantly amplified the demand for highly efficient on-chip communication topologies in today's accelerator platforms. AI accelerators feature several processing parts, memory hierarchies, tensor engines and special computational units that need to move large amounts of data with low latency and optimal efficiency. Traditional bus-based communication systems cannot provide the bandwidth and scalability requirements of modern AI workloads, particularly deep learning training and inference with large neural network models. This leads to the necessity of Network-on-Chip (NoC) architectures as a vital solution for scalable and high-performance interconnectivity in AI accelerator systems. High-bandwidth NoCs provide efficient packet-based communication across distributed processing nodes, leading to enhanced parallelism, better resource utilization, and improved overall system throughput. The design of an effective NoC for AI accelerator platforms has become an important issue of scalability, communication latency, congestion control, power efficiency, and dependability. More processing cores and memory interfaces make low-latency communication harder to preserve and network traffic congestion harder to avoid. Further, interconnect fabrics with high bandwidths have large power consumption and may limit the temperature for the progress of semiconductor technology. However, the use of NoC in large-scale AI systems is hindered by reliability difficulties such as packet loss, deadlocks, fault tolerance, and traffic imbalance. This paper presents a complete methodology for the design of high-bandwidth Network-on-Chip (NoC) for AI accelerator platforms, focusing on scalable topology selection, adaptive routing algorithms, congestion-aware traffic management, low-power communication techniques, and methods for enhancing dependability. We demonstrate a case study of a practical AI accelerator to explain implementation considerations and performance trade-offs for real AI workloads. Experimental research shows that the proposed methodology may significantly optimize bandwidth utilization, reduce communication latency, alleviate congestion hotspots and enhance energy efficiency compared to typical NoC systems.