Making large-scale support vector machine learning practical
Advances in kernel methods
Energy-efficient signal processing via algorithmic noise-tolerance
ISLPED '99 Proceedings of the 1999 international symposium on Low power electronics and design
Fault Injection Techniques and Tools
Computer
Best-effort parallel execution framework for Recognition and mining applications
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
CASES '09 Proceedings of the 2009 international conference on Compilers, architecture, and synthesis for embedded systems
Green: a framework for supporting energy-conscious programming using controlled approximation
PLDI '10 Proceedings of the 2010 ACM SIGPLAN conference on Programming language design and implementation
Scalable effort hardware design: exploiting algorithmic resilience for energy efficiency
Proceedings of the 47th Design Automation Conference
Best-effort computing: re-thinking parallel software and hardware
Proceedings of the 47th Design Automation Conference
ERSA: error resilient system architecture for probabilistic applications
Proceedings of the Conference on Design, Automation and Test in Europe
Trading Accuracy for Power with an Underdesigned Multiplier Architecture
VLSID '11 Proceedings of the 2011 24th International Conference on VLSI Design
IMPACT: imprecise adders for low-power approximate computing
Proceedings of the 17th IEEE/ACM international symposium on Low-power electronics and design
Dynamic effort scaling: managing the quality-efficiency tradeoff
Proceedings of the 48th Design Automation Conference
Managing performance vs. accuracy trade-offs with loop perforation
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Benchmarking modern multiprocessors
Benchmarking modern multiprocessors
Assuring application-level correctness against soft errors
Proceedings of the International Conference on Computer-Aided Design
Architecture support for disciplined approximate programming
ASPLOS XVII Proceedings of the seventeenth international conference on Architectural Support for Programming Languages and Operating Systems
SALSA: systematic logic synthesis of approximate circuits
Proceedings of the 49th Annual Design Automation Conference
Quality programmable vector processors for approximate computing
Proceedings of the 46th Annual IEEE/ACM International Symposium on Microarchitecture
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Approximate computing is an emerging design paradigm that enables highly efficient hardware and software implementations by exploiting the inherent resilience of applications to in-exactness in their computations. Previous work in this area has demonstrated the potential for significant energy and performance improvements, but largely consists of ad hoc techniques that have been applied to a small number of applications. Taking approximate computing closer to mainstream adoption requires (i) a deeper understanding of inherent application resilience across a broader range of applications (ii) tools that can quantitatively establish the inherent resilience of an application, and (iii) methods to quickly assess the potential of various approximate computing techniques for a given application. We make two key contributions in this direction. Our primary contribution is the analysis and characterization of inherent application resilience present in a suite of 12 widely used applications from the domains of recognition, data mining, and search. Based on this analysis, we present several new insights into the nature of resilience and its relationship to various key application characteristics. To facilitate our analysis, we propose a systematic framework for Application Resilience Characterization (ARC) that (a) partitions an application into resilient and sensitive parts and (b) characterizes the resilient parts using approximation models that abstract a wide range of approximate computing techniques. We believe that the key insights that we present can help shape further research in the area of approximate computing, while automatic resilience characterization frameworks such as ARC can greatly aid designers in the adoption approximate computing.