Due to reduced manufacturing yields, traditional monolithic chips cannot keep up with the compute, memory, and communication demands of data-intensive applications, such as rapidly growing deep neural network (DNN) models. Chiplet-based architectures offer a cost-effective and scalable solution by integrating smaller chiplets via a network-on-interposer (NoI). Fast and accurate simulation approaches are critical to unlocking this potential, but existing methods lack the required accuracy, speed, and flexibility. To address this need, this work presents CHIPSIM, a comprehensive co-simulation framework designed for parallel DNN execution on chiplet-based systems. CHIPSIM concurrently models computation and communication, accurately capturing network contention and pipelining effects that conventional simulators overlook. Furthermore, it profiles the chiplet and NoI power consumptions at microsecond granularity for precise transient thermal analysis. Extensive evaluations with homogeneous/heterogeneous chiplets and different NoI architectures demonstrate the framework's versatility, up to 340% accuracy improvement, and power/thermal analysis capability.

翻译：由于制造良率下降，传统单片芯片难以满足数据密集型应用（如快速增长的深度神经网络模型）对计算、存储和通信的需求。基于芯粒的架构通过硅中介层网络集成更小的芯粒，提供了一种经济高效且可扩展的解决方案。快速而精确的仿真方法对于释放这一潜力至关重要，但现有方法在精度、速度和灵活性方面存在不足。为应对这一需求，本研究提出了CHIPSIM——一个专为基于芯粒系统上并行深度神经网络执行设计的综合协同仿真框架。CHIPSIM同步建模计算与通信过程，能准确捕捉传统仿真器忽略的网络争用与流水线效应。此外，该框架以微秒级粒度分析芯粒与硅中介层网络的功耗，支持精确的瞬态热分析。通过对同构/异构芯粒及不同硅中介层网络架构的广泛评估，验证了该框架的通用性、高达340%的精度提升能力以及功耗/热分析功能。