Abstract:
Approximate computing recently arises due to its success in many error-tolerant applications such as multimedia applications. Various approximation methods have demonstra...Show MoreMetadata
Abstract:
Approximate computing recently arises due to its success in many error-tolerant applications such as multimedia applications. Various approximation methods have demonstrated the effectiveness of relaxing precision requirements in a specific arithmetic unit. This provides a basis for exploring simultaneous use of multiple approximate units to improve efficiency. In this paper, we aim to identify a proper approximation configuration of approximate units in a program to minimize energy consumption while meeting quality constraints. To do this, we formulate a constrained optimization problem and develop a tool called WOAxC that uses genetic algorithm to solve this problem. WOAxC considers the impact of different input workload on the application quality. We evaluate the efficacy of WOAxC in minimizing the energy consumption of several image processing applications with varying size (i.e., number of operations), workload (i.e., input datasets), and quality constraints. Our evaluation shows that the configuration provided by WOAxC for a system with multiple approximate units improves the energy efficiency by, on average, 79.6%, 77.4%, and 70.94% for quality loss of 5%, 2.5% and 0% (no loss), respectively. To the best of our knowledge, WOAxC is the first workload-aware approach to identify proper approximation configuration for energy minimization under quality guarantee.
Date of Conference: 01-05 February 2021
Date Added to IEEE Xplore: 16 July 2021
ISBN Information:
ISSN Information:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Approximate Computation ,
- Energy Consumption ,
- Optimization Problem ,
- Loss Of Quality ,
- Specific Units ,
- Proper Estimation ,
- Image Processing Applications ,
- Quality Guarantee ,
- Multimedia Applications ,
- Proper Configuration ,
- Quality Constraints ,
- Machine Learning ,
- Loss Of Generality ,
- Energy Conservation ,
- Evolutionary Algorithms ,
- Can Survive ,
- Search Space ,
- Fitness Function ,
- Butterfly ,
- Yield Quality ,
- Sobel Operator ,
- Error Metrics ,
- Error Tolerance ,
- Program Level ,
- Search Phase ,
- Heuristic Algorithm ,
- Heuristic Method ,
- Termination Condition ,
- Solution Space
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Approximate Computation ,
- Energy Consumption ,
- Optimization Problem ,
- Loss Of Quality ,
- Specific Units ,
- Proper Estimation ,
- Image Processing Applications ,
- Quality Guarantee ,
- Multimedia Applications ,
- Proper Configuration ,
- Quality Constraints ,
- Machine Learning ,
- Loss Of Generality ,
- Energy Conservation ,
- Evolutionary Algorithms ,
- Can Survive ,
- Search Space ,
- Fitness Function ,
- Butterfly ,
- Yield Quality ,
- Sobel Operator ,
- Error Metrics ,
- Error Tolerance ,
- Program Level ,
- Search Phase ,
- Heuristic Algorithm ,
- Heuristic Method ,
- Termination Condition ,
- Solution Space