EDBT 2026 Demo / reviewers in the wild / expert
Thorben Fetz
dblp:317/6998
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—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 |
Emerging computing paradigms · 28% Memory systems · 28% Hardware reliability and fault tolerance · 28% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Hardware reliability and fault tolerance
fault injection |
0.7 | 1 | 2023 | Fault Injection in Native Logic-in-Memory Computation on Neuromorphic Hardware · DAC 2023 |
Memory systems › processing-in-memory
logic-in-memory |
0.7 | 1 | 2023 | Fault Injection in Native Logic-in-Memory Computation on Neuromorphic Hardware · DAC 2023 |
Emerging computing paradigms
neuromorphic computing |
0.7 | 1 | 2023 | Fault Injection in Native Logic-in-Memory Computation on Neuromorphic Hardware · DAC 2023 |
Hardware accelerators and domain-specific architectures › machine learning accelerator › neural network accelerator
binary neural network accelerator |
0.2 | 1 | 2023 | Fault Injection in Native Logic-in-Memory Computation on Neuromorphic Hardware · DAC 2023 |
Hardware accelerators and domain-specific architectures
machine learning accelerator |
0.2 | 1 | 2023 | Fault Injection in Native Logic-in-Memory Computation on Neuromorphic Hardware · DAC 2023 |
Methods — techniques the papers use, named apart from their topics
memristive crossbar simulation · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Fault Injection in Native Logic-in-Memory Computation on Neuromorphic HardwareabstractLogic-in-memory (LIM) describes the execution of logic gates within memristive crossbar structures, promising to improve performance and energy efficiency. Utilizing only binary values, LIM particularly excels in accelerating binary neural networks, shifting it in the focus of edge applications. Considering its potential, the impact of faults on BNNs accelerated with LIM still lacks investigation. In this paper, we propose faulty logic-in-memory (FLIM), a fault injection platform capable of executing full-fledged BNNs on LIM while injecting in-field faults. The results show that FLIM runs a single MNIST picture 66754× faster than the state of the art by offering a fine-grained fault injection methodology. Felix Staudigl, Thorben Fetz, Rebecca Pelke, Dominik Germek, Jan Moritz Joseph, Letícia Maria Veiras Bolzani, Rainer Leupers |
DAC | 2 |