Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration

Hasan Genc

Room 204
[ Abstract ]
Mon 29 Aug 1 p.m. PDT — 5 p.m. PDT


We present a tutorial that teaches users how to perform full-system, full-stack DNN accelerator evaluation using the Gemmini [1, 2] platform. Gemmini allows users to evaluate how a DNN hardware accelerator interacts with external components, like the cache hierarchy or virtual address translation scheme, to affect performance across the hardware-software-system stack.

With Gemmini, users can generate a variety of different DNN hardware accelerators, with different underlying system, SoC, and programming stack components. Users can evaluate the performance of their hardware accelerators on end-to-end workloads in a real-world system context, exposing how different system components, like the cache hierarchy, virtual address translation scheme, or operating system, impact performance in subtle but noticeable ways. Gemmini also allows users to program their applications at different “levels” of the programming stack, from high-level model compilation to low-level direct machine configuration. Overall, Gemmini enables users to explore and evaluate a variety of different DNN accelerator and system configurations, exposing how these different parameters interact to impact end-to-end performance and efficiency.

Gemmini has been presented previously at DAC 2021, where it won the Best Paper award, as well as at an IISWC 2021 tutorial.

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