Monad: Towards Cost-effective Specialization for Chiplet-based Spatial Accelerators
arxiv(2023)
摘要
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
the state-of-the-art chiplet-based accelerators, Simba and NN-Baton,
respectively.
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