Approximate computing has applications in areas such as image processing, neural computation, distributed systems, and real-time systems, where the results may be acceptable in the presence of controlled levels of error. The promise of approximate computing is in its ability to render just enough performance to meet quality constraints. However, going from this theoretical promise to a practical implementation requires a clear comprehension of the system requirements and matching them to the design of approximations as the system is implemented. This involves the tasks of (a) identifying the design space of potential approximations, (b) modeling the injected error as a function of the level of approximation, and (c) optimizing the system over the design space to maximize a metric, typically the power savings, under constraints on the maximum allowable degradation. Often, the error may be introduced at a low level of design (e.g., at the level of a full adder) but its impact must be percolated up to system-level error metrics (e.g., PSNR in a compressed image), and a practical approach must devise a coherent and quantifiable way of translating between error/power tradeoffs at all levels of design.
|Original language||English (US)|
|Title of host publication||Proceedings of the 2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems Companion, CASES 2017|
|Publisher||Association for Computing Machinery, Inc|
|State||Published - Oct 15 2017|
|Event||2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems, CASES 2017 - Seoul, Korea, Republic of|
Duration: Oct 15 2017 → Oct 20 2017
|Name||Proceedings of the 2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems Companion, CASES 2017|
|Other||2017 International Conference on Compilers, Architectures and Synthesis for Embedded Systems, CASES 2017|
|Country/Territory||Korea, Republic of|
|Period||10/15/17 → 10/20/17|
Bibliographical noteFunding Information:
∗This work was supported in part by the NSF under awards CCF-1162267, CCF-1525925, and CCF-1525749.
- Approximate computing
- Error resilience