EDBT 2026 Demo / reviewers in the wild / expert
Veronique Sousa
dblp:92/11213
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Applied, interdisciplinary, general and emerging computing · 1
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 |
Memory systems · 81% Storage systems · 19% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Memory systems › non-volatile memory
emerging nonvolatile memory |
0.3 | 1 | 2017 | Phase Change and Magnetic Memories for Solid-State Drive Applications · Proc. IEEE 2017 |
Memory systems
magnetic memory |
0.3 | 1 | 2017 | Phase Change and Magnetic Memories for Solid-State Drive Applications · Proc. IEEE 2017 |
Memory systems
non-volatile memory |
0.3 | 1 | 2017 | Phase Change and Magnetic Memories for Solid-State Drive Applications · Proc. IEEE 2017 |
Memory systems › non-volatile memory
phase change memory |
0.3 | 1 | 2017 | Phase Change and Magnetic Memories for Solid-State Drive Applications · Proc. IEEE 2017 |
Storage systems › flash and SSD
solid-state drive |
0.3 | 1 | 2017 | Phase Change and Magnetic Memories for Solid-State Drive Applications · Proc. IEEE 2017 |
Memory systems › non-volatile memory
storage class memory |
0.1 | 1 | 2017 | Phase Change and Magnetic Memories for Solid-State Drive Applications · Proc. IEEE 2017 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Phase Change and Magnetic Memories for Solid-State Drive ApplicationsabstractThe state-of-the-art solid-state drives (SSDs) now heterogeneously integrate NAND Flash and dynamic random access memories (DRAMs) to partially hide the limitation of the nonvolatile memory technology. However, due to the increased request for storage density coupled with performance that positions the storage tier closer to the latency of the processing elements, NAND Flash are becoming a serious bottleneck. DRAM as well are a limitation in the SSD reliability due to their vulnerability to the power loss events. Several emerging memory technologies are candidate to replace them, namely the storage class memories. Phase change memories and magnetic memories fall into this category. In this work, we review both technologies from the perspective of their possible application in future disk drives, opening up new computation paradigms as well as improving the storage characteristics in terms of latency and reliability. Cristian Zambelli, Gabriele Navarro, Veronique Sousa, Ioan Lucian Prejbeanu, Luca Perniola |
Proc. IEEE | 3 |
| 2011 | Phase change memory for synaptic plasticity application in neuromorphic systemsabstractIn this paper, we show that Phase Change Memory (PCM) can be used to emulate specific functions of a biological synapse similar to Long Term Potentiation (LTP) and Long Term Depression (LTD) plasticity effects. The dependence of synaptic weight on programming pulse width and pulse amplitude is shown experimentally for the PCM devices. Different combinations of consecutive LTD and LTP events have been experimentally demonstrated and analyzed for the PCM synapse. Manan Suri, Veronique Sousa, Luca Perniola, Dominique Vuillaume, Barbara De Salvo |
IJCNN | 2 |