Monad: Towards Cost-effective Specialization for Chiplet-based Spatial Accelerators

TL;DR

Monad is a cost-aware specialization framework for chiplet-based spatial accelerators that optimizes architectural and integration tradeoffs, significantly reducing energy-delay product compared to existing solutions.

Contribution

It introduces a systematic ML-based approach to explore the combined design space of architecture and integration for chiplet accelerators, considering cost and performance tradeoffs.

Findings

Achieves 16% EDP reduction over Simba

Achieves 30% EDP reduction over NN-Baton

Models non-uniform dataflow, pipelining, and communication costs

Abstract

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.