Advanced packaging offers a new design paradigm in the post-Moore era, where many smaller chiplets could be assembled into a large system to achieve extreme scalability and cost reduction. Recently proposed chiplet-based DNN accelerators demonstrate its effectiveness but fail to explore the tradeoffs between PPA and the fabrication cost. Specifically, we should explore both the architectural design space for individual chiplets and different integration options to assemble these chiplets. More advanced (and costly) packaging technology can enhance connectivity, but may meanwhile reduce the budget on chiplets. In this paper, we propose ALEGO, an architecture-and-integration co-design approach for chiplet-based spatial accelerators. Based on a heterogeneous integration paradigm, ALEGO can optimize each chiplet design for different workloads to achieve better efficiency. The co-design is enabled by using uniform architecture and integration encoding and a systematic design space exploration flow. We develop an architecture modeling framework and an ML-based approach to optimize the design parameters. Experiments demonstrate that ALEGO achieves 24%, 16%, or 23% improvement in latency, energy, and cost, respectively compared with the best of separate architecture or integration optimization.
翻译:在后摩尔时代,高级包装提供了一个新的设计范式,在这个模式中,许多较小的芯片可以组装成一个大型系统,以实现极端的可缩缩和降低成本。最近提出的基于芯片的 DNN 加速器显示其有效性,但未能探索PPPA与制造成本之间的权衡。具体地说,我们应该探索单个芯片的建筑设计空间和不同集成选项,以组装这些芯片。更先进的(和昂贵的)包装技术可以增强连通性,但同时可能减少芯片的预算。在这份文件中,我们提议对基于芯片的空间加速器采用建筑和一体化共同设计方法。根据混杂的整合模式,ALEGO可以优化每个芯片的设计设计,以提高效率。通过使用统一的架构、集成编码和系统设计空间探索流程,我们开发一个建筑模型框架和基于ML的方法来优化设计参数。我们进行实验表明,ALEGO在固定、能源和成本结构的整合方面分别实现了24 %、16 %或23%的改进。