The nature of statistical learning theory
The nature of statistical learning theory
Energy-efficient signal processing via algorithmic noise-tolerance
ISLPED '99 Proceedings of the 1999 international symposium on Low power electronics and design
Reliable and energy-efficient digital signal processing
Proceedings of the 39th annual Design Automation Conference
Low-Power CMOS Design
Dynamic Power Management: Design Techniques and CAD Tools
Dynamic Power Management: Design Techniques and CAD Tools
An ATPG for Threshold Testing: Obtaining Acceptable Yield in Future Processes
ITC '02 Proceedings of the 2002 IEEE International Test Conference
Proceedings of the 2003 international conference on Compilers, architecture and synthesis for embedded systems
Razor: A Low-Power Pipeline Based on Circuit-Level Timing Speculation
Proceedings of the 36th annual IEEE/ACM International Symposium on Microarchitecture
Dynamic complexity scaling for real-time H.264/AVC video encoding
Proceedings of the 15th international conference on Multimedia
Error-resilient motion estimation architecture
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Best-effort parallel execution framework for Recognition and mining applications
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Proceedings of the 14th ACM/IEEE international symposium on Low power electronics and design
CASES '09 Proceedings of the 2009 international conference on Compilers, architecture, and synthesis for embedded systems
Phoenix rebirth: Scalable MapReduce on a large-scale shared-memory system
IISWC '09 Proceedings of the 2009 IEEE International Symposium on Workload Characterization (IISWC)
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
Proceedings of the 47th Design Automation Conference
Best-effort computing: re-thinking parallel software and hardware
Proceedings of the 47th Design Automation Conference
Hardware that produces bounded rather than exact results
Proceedings of the 47th Design Automation Conference
Proceedings of the 16th ACM/IEEE international symposium on Low power electronics and design
ERSA: error resilient system architecture for probabilistic applications
Proceedings of the Conference on Design, Automation and Test in Europe
Proceedings of the Conference on Design, Automation and Test in Europe
Dynamic knobs for responsive power-aware computing
Proceedings of the sixteenth international conference on Architectural support for programming languages and operating systems
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
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
| Hi-index | 0.00 |
Several current and emerging applications do not have a unique result for a given input; rather, functional correctness is defined in terms of output quality. Recently proposed design techniques exploit the inherent resilience of such applications and achieve improved efficiency (energy or performance) by foregoing correct execution of all the constituent computations. Hardware and software systems that are thus designed may be viewed as scalable effort systems, since they offer the capability to modulate the effort that they expend towards computation, thereby allowing for trade-offs between output quality and efficiency. We propose the concept of Dynamic Effort Scaling (DES), which refers to dynamic management of the control knobs that are exposed by scalable effort systems. We argue the need for DES by observing that the degree of resilience often varies significantly across applications, across datasets, and even within a dataset. We propose a general conceptual framework for DES by formulating it as a feedback control problem, wherein the scaling mechanisms are regulated with the goal of maintaining output quality at or above a specified limit. We present an implementation of Dynamic Effort Scaling for recognition and mining applications and evaluate it for the support vector machines and K-means clustering algorithms under various application scenarios and datasets. Our results clearly demonstrate the benefits of the proposed approach---statically setting the scaling mechanisms leads to either significant error overshoot or significant opportunities for energy savings left on the table unexploited. In contrast, DES is able to effectively regulate the output quality while maximally exploiting the time-varying resiliency in the workload.