Can I read Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration on EtoBox?

Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration by Genc, Hasan; Kim, Seah; Amid, Alon; Haj-Ali, Ameer; Iyer, Vighnesh; Prakash, Pranav; Zhao, Jerry; Grubb, Daniel; Liew, Harrison; Mao, Howard; Ou, Albert; Schmidt, Colin; Steffl, Samuel; Wright, John; Stoica, Ion; Ragan-Kelley, Jonathan; Asanovic, Krste; Nikolic, Borivoje; Shao, Yakun Sophia is a scholarly article available to read on EtoBox.

What is Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration about?

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

Author

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

Published

2019

Language

EN