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| Content Provider | ACM Digital Library |
|---|---|
| Author | Raha, Arnab Raghunathan, Vijay |
| Abstract | The intrinsic error resilience exhibited by emerging application domains enables a new dimension for energy optimization of computing systems, namely the introduction of a controlled amount of approximations during system operation in exchange for substantial energy savings. Prior work in the area of approximate computing has focused on individual subsystems of a computing system, e.g., the computational subsystem or the memory subsystem. Since they focus only on individual subsystems, these techniques are unable to exploit the large energy-saving opportunities that stem from adopting a full-system perspective and approximating multiple subsystems of a computing platform simultaneously in a coordinated manner. This paper proposes a systematic methodology to perform joint approximations across different subsystems, leading to significant energy benefits compared to approximating individual subsystems in isolation. We use the example of a smart camera system that executes various computer vision and image processing applications to illustrate how the sensing, memory, and processing subsystems can all be approximated synergistically. We have implemented such an approximate smart camera system using an Altera Stratix IV GX FPGA development board, a Terasic TRDB-D5M 5 Megapixel camera module, and a 1GB DDR3 SODIMM module. Experimental results obtained using six application benchmarks demonstrate significant energy savings (around 7.5X on average) for minimal (< 1%) loss in application quality. Compared to approximating a single subsystem, the proposed full-system approximation methodology achieves additional energy benefits of 3.5X - 5.5X on average for minimal (< 1%) quality loss. |
| Starting Page | 1 |
| Ending Page | 6 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781450349277 |
| DOI | 10.1145/3061639.3062333 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-06-18 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
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