EDBT 2026 Demo / reviewers in the wild / expert
Piotr Ratuszniak
dblp:06/8115
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0001-8792-836XORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1Databases, data management, data science and information retrieval · 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 · 56% High-performance computing · 44% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
High-performance computing › numerical linear algebra
GEMM |
0.3 | 1 | 2018 | A Customizable Matrix Multiplication Framework for the Intel HARPv2 Xeon+FPGA Platform: A Deep Learning Case Study · FPGA 2018 |
Hardware accelerators and domain-specific architectures › sparse matrix multiplication accelerator
matrix multiplication accelerator |
0.3 | 1 | 2018 | A Customizable Matrix Multiplication Framework for the Intel HARPv2 Xeon+FPGA Platform: A Deep Learning Case Study · FPGA 2018 |
Hardware accelerators and domain-specific architectures
deep learning |
0.1 | 1 | 2018 | A Customizable Matrix Multiplication Framework for the Intel HARPv2 Xeon+FPGA Platform: A Deep Learning Case Study · FPGA 2018 |
Methods — techniques the papers use, named apart from their topics
reduced precision arithmetic · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | DAPHNE: An Open and Extensible System Infrastructure for Integrated Data Analysis Pipelines
Patrick Damme, Marius Birkenbach, Constantinos Bitsakos, Matthias Boehm 0001, Philippe Bonnet, Florina M. Ciorba, Mark Dokter, Pawel Dowgiallo, Ahmed Eleliemy, Christian Färber, Georgios I. Goumas, Dirk Habich, Niclas Hedam, Marlies Hofer, Kevin Innerebner, Vasileios Karakostas, Roman Kern, Tomaz Kosar, Alexander Krause 0001, Daniel Krems, Andreas Laber, Wolfgang Lehner, Eric Mier, Marcus Paradies, Bernhard Peischl, Gabrielle Poerwawinata, Stratos Psomadakis, Tilmann Rabl, Piotr Ratuszniak, Pedro Silva 0011, Nikolai Skuppin, Andreas Starzacher, Benjamin Steinwender, Ilin Tolovski, Pinar Tözün, Wojciech Ulatowski, Yuanyuan Wang 0002, Izajasz P. Wrosz, Ales Zamuda, Ce Zhang 0001, Xiao Xiang Zhu 0001 |
CIDR | 30 |
| 2018 | A Customizable Matrix Multiplication Framework for the Intel HARPv2 Xeon+FPGA Platform: A Deep Learning Case StudyabstractGeneral Matrix to Matrix multiplication (GEMM) is the cornerstone for a wide gamut of applications in high performance computing (HPC), scientific computing (SC) and more recently, deep learning. In this work, we present a customizable matrix multiplication framework for the Intel HARPv2 CPU+FPGA platform that includes support for both traditional single precision floating point and reduced precision workloads. Our framework supports arbitrary size GEMMs and consists of two parts: (1) a simple application programming interface (API) for easy configuration and integration into existing software and (2) a highly customizable hardware template. The API provides both compile and runtime options for controlling key aspects of the hardware template including dynamic precision switching; interleaving and block size control; and fused deep learning specific operations. The framework currently supports single precision floating point (FP32), 16, 8, 4 and 2 bit Integer and Fixed Point (INT16, INT8, INT4, INT2) and more exotic data types for deep learning workloads: INT16xTernary, INT8xTernary, BinaryxBinary. Duncan J. M. Moss, Krishnan Srivatsan, Eriko Nurvitadhi, Piotr Ratuszniak, Jaewoong Sim, Asit K. Mishra, Debbie Marr, Suchit Subhaschandra, Philip H. W. Leong |
FPGA | 4 |