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Designing Domain-Specific Heterogeneous Architectures from Dataflow Programs

Savas, Süleyman, Ul-Abdin, Zain, Nordström, Tomas

Artikel i tidskrift (Refereegranskat)


The last ten years have seen performance and power requirements pushing computer architectures using only a single core towards so-called manycore systems with hundreds of cores on a single chip. To further increase performance and energy efficiency, we are now seeing the development of heterogeneous architectures with specialized and accelerated cores. However, designing these heterogeneous systems is a challenging task due to their inherent complexity. We proposed an approach for designing domain-specific heterogeneous architectures based on instruction augmentation through the integration of hardware accelerators into simple cores. These hardware accelerators were determined based on their common use among applications within a certain domain.The objective was to generate heterogeneous architectures by integrating many of these accelerated cores and connecting them with a network-on-chip. The proposed approach aimed to ease the design of heterogeneous manycore architectures—and, consequently, exploration of the design space—by automating the design steps. To evaluate our approach, we enhanced our software tool chain with a tool that can generate accelerated cores from dataflow programs. This new tool chain was evaluated with the aid of two use cases: radar signal processing and mobile baseband processing. We could achieve an approximately 4x improvement in performance, while executing complete applications on the augmented cores with a small impact (2.5–13%) on area usage. The generated accelerators are competitive, achieving more than 90% of the performance of hand-written implementations.

Nyckelord: heterogeneous architecture design; risc-v; dataflow; QR decomposition; domain-specific processor; accelerator; Autofocus; hardware software co-design

Citera: Savas, Süleyman, Ul-Abdin, Zain & Nordström, Tomas, Designing Domain-Specific Heterogeneous Architectures from Dataflow Programs, Computers., 7:2, 2018https://doi.org/10.3390/computers7020027