Name
Title | ||
|---|---|---|
Synergistic Approximation of Computation and Memory Subsystems for Error-Resilient Applications. |
Abstract | ||
|---|---|---|
Approximate computing is a new design paradigm that exploits the intrinsic error resilience exhibited by emerging applications to significantly improve their energy efficiency and performance. Prior work in this domain has proposed approximation techniques targeting either the computational subsystem or the memory subsystem. For the first time, this letter proposes a methodology to perform synergistic approximations across the computation and memory subsystems together that results in a significant improvement in energy consumption compared to the case when the subsystems are approximated independently. We implemented our proposed methodology using an Altera Stratix IV GX FPGA based Terasic TR4-230 development board containing a 1GB DDR3 DRAM module, which executes three error-resilient benchmarks. Experimental results demonstrate energy improvements in the range of $2.05\\boldsymbol {\\times }$ - $4.45\\boldsymbol {\\times }$ for minimal loss in application quality (<1%). Compared to individual approximations, our technique achieves an additional $1.6\\boldsymbol {\\times }$ - $2.35\\boldsymbol {\\times }$ energy savings for the same quality specifications. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/LES.2017.2658566 | Embedded Systems Letters |
Keywords | Field | DocType |
Random access memory,Kernel,Energy consumption,Benchmark testing,Degradation,Memory management,Indexes | Dram,Stratix,Computer science,Efficient energy use,Parallel computing,Field-programmable gate array,Real-time computing,Memory management,Energy consumption,Benchmark (computing),Computation | Journal |
Volume | Issue | ISSN |
9 | 1 | 1943-0663 |
Citations | PageRank | References |
1 | 0.36 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arnab Raha | 1 | 197 | 19.45 |
| Vijay Raghunathan | 2 | 1932 | 170.13 |