VLDB 2026 Research / reviewers in the wild / expert
Andrew M. Keller
dblp:175/6231
· DBLP profile ↗
4ranked-venue papers
2as first author
1since 2021 · last 2022
0000-0002-6285-5288ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 4 · 2 first-author · 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
2 papers |
Hardware reliability and fault tolerance · 82% Reconfigurable computing and FPGAs · 10% Cloud and datacenter computing · 8% |
Topics — the 8 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Hardware reliability and fault tolerance
soft errors |
0.4 | 1 | 2019 | Impact of Soft Errors on Large-Scale FPGA Cloud Computing · FPGA 2019 |
Hardware reliability and fault tolerance › radiation effects
radiation-induced faults |
0.2 | 1 | 2016 | SEU Mitigation and Validation of the LEON3 Soft Processor Using Triple Modular Redundancy for Space Processing · FPGA 2016 |
Hardware reliability and fault tolerance › soft errors
single-event upset |
0.2 | 1 | 2016 | SEU Mitigation and Validation of the LEON3 Soft Processor Using Triple Modular Redundancy for Space Processing · FPGA 2016 |
Hardware reliability and fault tolerance › redundancy › modular redundancy
triple modular redundancy |
0.2 | 1 | 2016 | SEU Mitigation and Validation of the LEON3 Soft Processor Using Triple Modular Redundancy for Space Processing · FPGA 2016 |
Hardware reliability and fault tolerance
fault injection |
0.1 | 1 | 2019 | Impact of Soft Errors on Large-Scale FPGA Cloud Computing · FPGA 2019 |
Cloud and datacenter computing › cloud infrastructure
FPGA-based cloud computing |
0.1 | 1 | 2019 | Impact of Soft Errors on Large-Scale FPGA Cloud Computing · FPGA 2019 |
Reconfigurable computing and FPGAs › FPGA-based processor implementation
soft-core processor |
0.1 | 1 | 2016 | SEU Mitigation and Validation of the LEON3 Soft Processor Using Triple Modular Redundancy for Space Processing · FPGA 2016 |
Reconfigurable computing and FPGAs › FPGA architecture
SRAM-based FPGA |
0.1 | 1 | 2016 | SEU Mitigation and Validation of the LEON3 Soft Processor Using Triple Modular Redundancy for Space Processing · FPGA 2016 |
Methods — techniques the papers use, named apart from their topics
fault injection · 0.6orbit failure rate estimation · 0.2heavy ion radiation testing · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | The Impact of Terrestrial Radiation on FPGAs in Data CentersabstractField programmable gate arrays (FPGAs) are used in large numbers in data centers around the world. They are used for cloud computing and computer networking. The most common type of FPGA used in data centers are re-programmable SRAM-based FPGAs. These devices offer potential performance and power consumption savings. A single device also carries a small susceptibility to radiation-induced soft errors, which can lead to unexpected behavior. This article examines the impact of terrestrial radiation on FPGAs in data centers. Results from artificial fault injection and accelerated radiation testing on several data-center-like FPGA applications are compared. A new fault injection scheme provides results that are more similar to radiation testing. Silent data corruption (SDC) is the most commonly observed failure mode followed by FPGA unavailable and host unresponsive. A hypothetical deployment of 100,000 FPGAs in Denver, Colorado, will experience upsets in configuration memory every half-hour on average and SDC failures every 0.5–11 days on average. Andrew M. Keller, Michael J. Wirthlin |
ACM Trans. Reconfigurable Technol. Syst. | 1 |
| 2019 | Impact of Soft Errors on Large-Scale FPGA Cloud ComputingabstractFPGAs are being used in large numbers within cloud computing to provide high-performance, low-power alternatives to more traditional computing structures. While FPGAs provide a number of important benefits to cloud computing environments, they are susceptible to radiation-induced soft errors, which can lead to silent data corruption or system instability. Although soft errors within a single FPGA occur infrequently, soft errors in large-scale FPGAs systems can occur at a relatively high rate. This paper investigates the failure rate of several FPGA applications running within an FPGA cloud computing node by performing fault injection experiments to determine the susceptibility of these applications to soft-errors. The results from these experiments suggest that silent data corruption will occur every few hours within a 100,000 node FPGA system and that such a system can only maintain high-levels of reliability for short periods of operation. These results suggest that soft-error detection and mitigation techniques may be needed in large-scale FPGA systems. Andrew M. Keller, Michael J. Wirthlin |
FPGA | 1 |
| 2018 | Improving the Effectiveness of TMR Designs on FPGAs with SEU-Aware Incremental PlacementabstractTMR combined with configuration scrubbing is an effective technique to mitigate against radiation-induced CRAM upsets on SRAM-based FPGAs. However, its effectiveness is limited by low-level common mode failures due to the physical mapping of a design to the FPGA device. This paper describes how common mode failures are introduced during the implementation process and introduces an approach for resolving them through a custom incremental placement tool for Xilinx 7-Series FPGAs. Multiple designs across multiple generations of devices are shown to be sensitive to common mode failures. Applying the incremental placement technique yields an improvement of 10,721x over an unmitigated design through fault-injection testing. Radiation testing is then performed to show that the of this technique is 91,500 days in GEO orbit, a 367x improvement over the unmitigated design and a 5x improvement over baseline TMR. Matthew Cannon, Andrew M. Keller, Michael J. Wirthlin |
FCCM | 2 |
| 2016 | SEU Mitigation and Validation of the LEON3 Soft Processor Using Triple Modular Redundancy for Space ProcessingabstractProcessors are an essential component in most satellite payload electronics and handle a variety of functions including command handling and data processing. There is growing interest in implementing soft processors on commercial FPGAs within satellites. Commercial FPGAs offer reconfigurability, large logic density, and I/O bandwidth; however, they are sensitive to ionizing radiation and systems developed for space must implement single-event upset mitigation to operate reliably. This paper investigates the improvements in reliability of a LEON3 soft processor operating on a SRAM-based FPGA when using triple-modular redundancy and other processor-specific mitigation techniques. The improvements in reliability provided by these techniques are validated with both fault injection and heavy ion radiation tests. The fault injection experiments indicate an improvement of 51× and the radiation testing results demonstrate an average improvement of 10×. Orbit failure rate estimations were computed and suggest that the TMR LEON3 processor has a mean-time to failure of over 76 years in a geosynchronous orbit. Michael J. Wirthlin, Andrew M. Keller, Chase McCloskey, Parker Ridd, David S. Lee, Jeffrey T. Draper |
FPGA | 2 |