Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration
Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration
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
To appear at the 58th IEEE/ACM Design Automation Conference (DAC), December 2021, San Francisco, CA, USA
- University of California System United States
- University of California, Berkeley United States
- University of California, San Francisco United States
- Massachusetts Institute of Technology United States
Performance (cs.PF), FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Performance, Computer Science - Distributed, Parallel, and Cluster Computing, Hardware Architecture (cs.AR), Distributed, Parallel, and Cluster Computing (cs.DC), Computer Science - Hardware Architecture, Machine Learning (cs.LG)
Performance (cs.PF), FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Performance, Computer Science - Distributed, Parallel, and Cluster Computing, Hardware Architecture (cs.AR), Distributed, Parallel, and Cluster Computing (cs.DC), Computer Science - Hardware Architecture, Machine Learning (cs.LG)
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).108 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 1% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
Citations provided by BIP!Popularity provided by BIP!