Demo Data from VLDB 2026 / reviewers in the wild / expert
Pranav Prakash
dblp:305/9161
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Hardware accelerators and domain-specific architectures · 50% Integrated circuit design · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Hardware accelerators and domain-specific architectures › machine learning accelerator
DNN accelerator |
0.5 | 1 | 2021 | Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration · DAC 2021 |
Integrated circuit design › system-on-chip
system-on-chip integration |
0.5 | 1 | 2021 | Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration · DAC 2021 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack IntegrationabstractDNN 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 Systemon-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. Hasan Genc, Seah Kim, Alon Amid, Ameer Haj-Ali, Vighnesh Iyer, Pranav Prakash, Jerry Zhao, Daniel Grubb, Harrison Liew, Howard Mao, Albert J. Ou, Colin Schmidt 0001, Samuel Steffl, John Charles Wright, Ion Stoica, Jonathan Ragan-Kelley, Krste Asanovic, Borivoje Nikolic, Sophia Shao |
DAC | 6 |