Marco Widmer

dblp:241/4304 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 first-author

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 · 61% Hardware reliability and fault tolerance · 30% Emerging computing paradigms · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Memory systems
DRAM
0.412019
FPGA-Based Emulation of Embedded DRAMs for Statistical Error Resilience Evaluation of Approximate Computing Systems · DAC 2019
Memory systems › DRAM › DRAM architecture
embedded DRAM
0.412019
FPGA-Based Emulation of Embedded DRAMs for Statistical Error Resilience Evaluation of Approximate Computing Systems · DAC 2019
Hardware reliability and fault tolerance
soft errors
0.412019
FPGA-Based Emulation of Embedded DRAMs for Statistical Error Resilience Evaluation of Approximate Computing Systems · DAC 2019
Emerging computing paradigms
approximate computing
0.112019
FPGA-Based Emulation of Embedded DRAMs for Statistical Error Resilience Evaluation of Approximate Computing Systems · DAC 2019

Methods — techniques the papers use, named apart from their topics

retention time modeling · 0.4FPGA-based emulation · 0.4
YearPublicationVenuePosition
2019 FPGA-Based Emulation of Embedded DRAMs for Statistical Error Resilience Evaluation of Approximate Computing Systems
abstract
Embedded DRAM (eDRAM) requires frequent power-hungry refresh according to the worst-case retention time across PVT variations to avoid data loss. Abandoning the error-free paradigm, by choosing sub-critical refresh rates that gracefully degrade the eDRAM content, unlocks considerable power-saving opportunities, but requires to understand the effect of stochastic memory errors at the system/application level. We propose an FPGA-based platform featuring faulty eDRAM emulation based on advanced retention time models and silicon measurements for statistical error resilience evaluation of applications in a complete embedded system. We analyze the statistical QoS for various benchmarks under different sub-critical refresh rates and retention time distributions.
Marco Widmer, Andrea Bonetti, Andreas Peter Burg
DAC1