Name
Abstract | ||
|---|---|---|
The key to increasing performance without a commensurate increase in power consumption in modern processors lies in increasing both parallelism and core specialization. Core specialization has been employed in the embedded space and is likely to play an important role in future heterogeneous multi-core architectures as well. In this paper, the face recognition application domain is employed as a case study to showcase an architectural design methodology which generates a specialized core with high performance and very low powercharacteristics. Specifically, we create "ASIC-like" execution flows to sustain the high memory parallelism generated within the core. The price of this benefit is a significant increase in compilation complexity. The crux of the problem is the need to co-schedule the often conflicting constraints of data access, data movement, and computation. A modular compiler approach that employs integer linear programming (ILP) based "interconnect-aware" instruction and data scheduling techniques to solve this problem is then described. The resulting core running the compiled code delivers a 1.65x throughput improvement over a high performance processor (Pentium 4) while simultaneously achieving an 80x energy-delay improvement over an energy-efficient processor (XScale) and performs real-time face recognition at embedded power budgets. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1289881.1289902 | CASES |
Keywords | Field | DocType |
high performance processor,resulting core,embedded system design,embedded power budget,commensurate increase,core specialization,high memory parallelism,high performance,specialized core,data access,data movement,face recognition case study,algorithms,compilers,real time,instruction scheduling,design,embedded system,embedded systems,face recognition | Computer architecture,Instruction scheduling,Computer science,Parallel computing,Compiler,Real-time computing,Compiled language,Integer programming,Application domain,Pentium,Modular design,Data access | Conference |
Citations | PageRank | References |
1 | 0.35 | 19 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karthik Ramani | 1 | 1328 | 81.38 |
| Al Davis | 2 | 986 | 54.47 |