The exponential growth of AI has created unprecedented demand for computational resources, pushing chip designs to the limit while simultaneously escalating the environmental footprint of computing. As the industry transitions toward heterogeneous integration (HI) to address the yield and cost challenges of monolithic scaling, minimizing the carbon cost of these complex HI systems becomes critical. To fully exploit HI, a co-design approach spanning application, architecture, chip, and packaging is essential. However, this creates a vast design space with competing objectives, specifically the trade-offs between performance, cost, and carbon footprint (CFP) for sustainability. CarbonPATH is an early-stage pathfinding framework designed to address this multi-objective challenge. It identifies optimized HI systems by co-designing workload mapping, architectural parameters, and packaging technologies, while treating sustainability as a first-class design constraint. The framework accounts for a wide range of factors, including compute and memory sizes, chiplet technology nodes, communication protocols, integration style (2D, 2.5D, 3D), operational CFP, embodied CFP, and interconnect type. Using simulated annealing, CarbonPATH explores this high-dimensional space to identify solutions that balance traditional metrics against environmental impact. By capturing interactions across applications, architectures, chiplets, and packaging, CarbonPATH uncovers system-level solutions that traditional methods often miss due to restrictive assumptions or limited scope.

翻译：人工智能的指数级增长对计算资源产生了前所未有的需求，在将芯片设计推向极限的同时，也加剧了计算的环境足迹。随着行业向异构集成转型以应对单片式扩展的良率和成本挑战，最小化这些复杂异构集成系统的碳成本变得至关重要。为充分发挥异构集成的潜力，跨越应用、架构、芯片和封装的协同设计方法必不可少。然而，这形成了一个具有竞争性目标的庞大设计空间，特别是在性能、成本与可持续性所需的碳足迹之间存在着权衡关系。CarbonPATH是一个旨在应对这一多目标挑战的早期路径探索框架。它通过协同设计工作负载映射、架构参数和封装技术来识别优化的异构集成系统，并将可持续性作为首要设计约束。该框架考虑了广泛的因素，包括计算与存储规模、芯粒技术节点、通信协议、集成方式（2D、2.5D、3D）、运行碳足迹、隐含碳足迹以及互连类型。CarbonPATH利用模拟退火算法探索这一高维空间，以识别能在传统指标与环境影响之间取得平衡的解决方案。通过捕捉应用、架构、芯粒和封装之间的相互作用，CarbonPATH揭示了传统方法因受限假设或有限范围而常忽视的系统级解决方案。