Name
Abstract | ||
|---|---|---|
Architecture customization is believed as one of the most promising methods to meet ever-increasing computing needs and power density limitations. This paper presents an approach to enhance a preliminary customizable core with some common architecture features, to adapt to the specific applications while keeping the programming flexibility. Those features include several effective software/hardware co-optimizing strategies, such as loop tiling, pre-fetching, cache customization, customized Single Instruction Multiple Data (SIMD) and Direct Memory Access (DMA), as well as the necessary ISA extensions. Currently we select stencil computation as the research target. Detailed tests of power-efficiency to evaluate the effect of all these optimizations comprehensively shows impressive performance speedup and power efficiency, even compared to X86, GPU and FPGA platforms. All these proposed customizations here could be applied to other computing applications. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ASAP.2014.6868656 | ASAP |
Keywords | Field | DocType |
prefetching,parallel processing,fpga platforms,architecture customization,dma,customizable processor,microprocessor chips,stencil computation,cache customization,high-performance computing,graphics processing units,x86,customized single instruction multiple data,programming flexibility,processor core customization approach,customizable core,multiprocessing systems,power density limitations,software/hardware cooptimizing strategies,file organisation,architecture features,software/hardware co-design,loop tiling,direct memory access,simd,computer architecture,field programmable gate arrays,gpu platforms | x86,Computer architecture,Cache,Computer science,Parallel computing,Stencil code,SIMD,Loop tiling,Direct memory access,Multi-core processor,Speedup,Embedded system | Conference |
ISSN | Citations | PageRank |
2160-0511 | 1 | 0.39 |
References | Authors | |
5 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanhua Li | 1 | 12 | 2.68 |
| Youhui Zhang | 2 | 202 | 28.36 |
| Jianfeng Yang | 3 | 1 | 0.39 |
| Wayne Luk | 4 | 3752 | 438.09 |
| Guangwen Yang | 5 | 599 | 92.40 |
| Weimin Zheng | 6 | 1889 | 182.48 |