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arXiv · 1911.09925

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

Hasan Genc·Seah Kim·Alon Amid·Ameer Haj-Ali·Vighnesh Iyer·Pranav Prakash·Jerry Zhao·Daniel Grubb·Harrison Liew·Howard Mao·Albert Ou·Colin Schmidt·Samuel Steffl·John Wright·Ion Stoica·Jonathan Ragan-Kelley·Krste Asanovic·Borivoje Nikolic·Yakun Sophia Shao
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Abstract

DNN accelerators are often developed and evaluated in isolation without considering the cross-stack, system-level effects in real-world environments. This makes it difficult to appreciate the impact of System-on-Chip (SoC) resource contention, OS overheads, and programming-stack inefficiencies on overall performance/energy-efficiency. To address this challenge, we present Gemmini, an open-source*, full-stack DNN accelerator generator. Gemmini generates a wide design-space of efficient ASIC accelerators from a flexible architectural template, together with flexible programming stacks and full SoCs with shared resources that capture system-level effects. Gemmini-generated accelerators have also been fabricated, delivering up to three orders-of-magnitude speedups over high-performance CPUs on various DNN benchmarks. * https://github.com/ucb-bar/gemmini

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Hasan Genc, Seah Kim, Alon Amid, Ameer Haj-Ali, Vighnesh Iyer, Pranav Prakash, Jerry Zhao, Daniel Grubb, Harrison Liew, Howard Mao, Albert Ou, Colin Schmidt, Samuel Steffl, John Wright, Ion Stoica, Jonathan Ragan-Kelley, Krste Asanovic, Borivoje Nikolic, Yakun Sophia Shao. 2019-11-22. Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration. https://arxiv.org/abs/1911.09925

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