Name
Abstract | ||
|---|---|---|
Approximate hardware trades acceptable error for improved performance and previous literature focuses on optimizing this trade-off in the hardware. We show in this paper that the application (i.e., the software) can be optimized for better accuracy without losing any performance benefits of the approximate hardware. We propose LAC: learned approximate computing as a method of tuning the application parameters to compensate for hardware errors. Our approach showed improvements across a variety of standard signal/image processing applications delivering an average improvement of 5.82db in PSNR and 0.23 in SSIM of the outputs. This translates to up to 87% power reduction and 83% area reduction for similar application quality. LAC allows the same approximate hardware to be used for multiple applications. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.23919/DATE54114.2022.9774521 | PROCEEDINGS OF THE 2022 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE 2022) |
Keywords | DocType | ISSN |
approximate computing, machine learning | Conference | 1530-1591 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vaibhav Gupta | 1 | 0 | 0.34 |
| Tianmu Li | 2 | 1 | 1.38 |
| Puneet Gupta | 3 | 1158 | 117.59 |