ERSA: Error Resilient System Architecture for probabilistic applications
By L. Leem, Hyungmin Cho, Jason Bau, Quinn Jacobson, Subhasish G. Mitra. Design, Automation and Test in Europe, 2010
349 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering, Hardware and Architecture, Software
View Paper: https://www.semanticscholar.org/paper/3c843f763eb360ddae26e2d0f60da7c6a5fdeceb
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
- Sparsh Mittal
- Kaushik Roy
- Anand Raghunathan
- Luis Ceze
- Adrian Sampson
- Karin Strauss
- Jacob Nelson
- Jie Han
In Hardware and Architecture
- Kaushik Roy
- Anand Raghunathan
- Luis Ceze
- Adrian Sampson
- Sparsh Mittal
- Karin Strauss
- Jacob Nelson
- Hadi Esmaeilzadeh
Citations in the corpus
- OCEAN (2014, methodology): “In this context, we memory word are flipped, as used in Leem et al. [2010].”
- Architecture support for disciplined approximate programming (2012, background): “Indeed, prior work has investigated hardware and algorithmic techniques for approximate execution [2, 12, 14, 16].”
- EnerJ: approximate data types for safe and general low-power computation (2011, background): “Many studies have shown that a variety of applications have a high tolerance to transient faults [8, 19, 21, 22, 25, 35].”
- Approximate storage in solid-state memories (2013, background): “Techniques under the umbrella of approximate computing [2, 8, 12, 14, 22, 24, 26, 38, 41] exploit this tolerance to trade off accuracy for performance or energy efficiency.”
- Neural Acceleration for General-Purpose Approximate Programs (2012, background): “This approach contrasts with previous work on approximate computation that extends conventional microarchitectures to support selective approximate execution, in-curring instruction bookkeeping overheads [1,8,11,29], or requires vastly different programming paradigms [4,24,26,32].”
- Approximate computing: An emerging paradigm for energy-efficient design (2013, background): “Approximate computing, hence, employs deterministic designs that produce imprecise results.”
- Analysis and characterization of inherent application resilience for approximate computing (2013, background): “of fault-tolerance from hardware defects or transient faults in hardware, by exploiting error masking at the application level, or by using low-cost algorithmic fault tolerance [13].”