Advanced packaging offers a new design paradigm in the post-Moore era, where many small chiplets can be assembled into a large system. Based on heterogeneous integration, a chiplet-based accelerator can be highly specialized for a specific workload, demonstrating extreme efficiency and cost reduction. To fully leverage this potential, it is critical to explore both the architectural design space for individual chiplets and different integration options to assemble these chiplets, which have yet to be fully exploited by existing proposals. This paper proposes Monad, a cost-aware specialization approach for chiplet-based spatial accelerators that explores the tradeoffs between PPA and fabrication costs. To evaluate a specialized system, we introduce a modeling framework considering the non-uniformity in dataflow, pipelining, and communications when executing multiple tensor workloads on different chiplets. We propose to combine the architecture and integration design space by uniformly encoding the design aspects for both spaces and exploring them with a systematic ML-based approach. The experiments demonstrate that Monad can achieve an average of 16% and 30% EDP reduction compared with the state-of-the-art chiplet-based accelerators, Simba and NN-Baton, respectively.

翻译：先进封装技术在后摩尔时代提供了一种新的设计范式，可将众多小型芯粒组装成大型系统。基于异构集成，芯粒级加速器可针对特定工作负载实现高度专业化，展现出极致效率与成本降低。为充分利用此潜力，需同时探索单个芯粒的架构设计空间与组装芯粒的不同集成方案——现有研究尚未充分挖掘这一领域。本文提出Monad，一种面向芯粒级空间加速器的成本感知专业化方法，旨在权衡PPA与制造成本。为评估专业化系统，我们引入考虑数据流非均匀性、流水线与通信的建模框架，该框架适用于在不同芯粒上执行多个张量工作负载的场景。我们通过统一编码架构与集成设计空间的双重维度，并采用系统性机器学习方法进行探索，从而将两者结合。实验表明，与当前最先进的芯粒级加速器Simba和NN-Baton相比，Monad平均可分别降低16%和30%的EDP。