Demo Data from VLDB 2026 / reviewers in the wild / expert
Curt John Bansil
dblp:398/7113
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2025
0009-0009-8881-487XORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021Software engineering, systems software and programming languages · 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 |
Performance modeling and evaluation · 38% GPUs and heterogeneous computing · 38% Hardware accelerators and domain-specific architectures · 23% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Performance modeling and evaluation
workload characterization |
0.9 | 1 | 2025 | Performance Analysis of GEMM Workloads on the AMD Versal Platform · FPGA 2025 |
Hardware accelerators and domain-specific architectures › machine learning accelerator
GEMM acceleration |
0.3 | 1 | 2025 | Performance Analysis of GEMM Workloads on the AMD Versal Platform · FPGA 2025 |
Hardware accelerators and domain-specific architectures
machine learning accelerator |
0.3 | 1 | 2025 | Performance Analysis of GEMM Workloads on the AMD Versal Platform · FPGA 2025 |
Methods — techniques the papers use, named apart from their topics
performance scaling analysis · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Performance Analysis of GEMM Workloads on the AMD Versal PlatformabstractAMD Versal is a new heterogeneous computing hardware architecture comprised of adaptive intelligence (AI) engines, programmable logic, and a processing system. General Matrix Multiplication (GEMM) is the fundamental building block of modern deep learning (DL) applications such as ChatGPT, and GEMM workloads can be mapped onto Versal in different ways, each with distinct trade-offs. This paper presents a thorough analysis of GEMM workloads of different shapes and sizes, showcasing performance artifacts associated with the AMD Versal architecture. Focusing on the unique aspects of the Versal architecture, multiple research questions related to performance scaling, sensitivity, and efficiency are explored. This paper aims to assist developers in the FPGA community looking to implement GEMM on AMD Versal by providing guidelines and insights for enhancing performance. Kaustubh Manohar, Venkata Guru Prasanth Mulleti, Curt John Bansil, Endri Taka, Aman Arora 0001 |
FPGA | 3 |
| 2025 | Performance Analysis of GEMM Workloads on the AMD Versal PlatformabstractAMD Versal is a new heterogeneous computing hardware architecture comprised of adaptive intelligence (AI) engines, programmable logic, and a processing system. General Matrix Multiplication (GEMM) is the fundamental building block of modern deep learning (DL) applications such as ChatGPT, and GEMM workloads can be mapped onto Versal in different ways, each with distinct trade-offs. This paper presents a thorough analysis of GEMM workloads of different shapes and sizes, showcasing performance artifacts associated with the AMD Versal architecture. Focusing on the unique aspects of the Versal architecture, multiple research questions related to performance scaling, sensitivity, and efficiency are explored. This paper aims to assist FPGA developers looking to implement GEMM on AMD Versal by providing insights for enhancing performance. Kaustubh Manohar, Venkata Guru Prashanth Mulleti, Curt John Bansil, Endri Taka, Aman Arora 0001 |
ISPASS | 3 |