上海交通大学 电子信息与电气工程学院,上海,200240
纸质出版:2019
移动端阅览
尹文志, 赵仲元, 毛志刚, 王琴, 绳伟光. 一种快速高效的粗粒度可重构架构编译框架[J]. 微电子学与计算机, 2019, 36(8): 45-48, 53.
YIN Wen-zhi, ZHAO Zhong-yuan, MAO Zhi-gang, et al. A fast and efficient compiler framework for CGRAs[J]. 2019, 36(8): 45-48.
利用硬件和软件协同的设计技术来进一步提高粗粒度可重构加速器在处理循环时的编译时间与面积效率(单位面积的性能).在硬件方面将处理单元内部的寄存器堆结构优化
用旁路互联的方式替代.软件方面基于这种结构提出了一种新颖
高效的循环映射算法.该算法相对于同期的研究算法
极大的缩小了搜索最优解决方案的空间.利用前向贪婪和反向回溯迭代运行
可以获得快速而又稳定的编译时间
同时保证了接近最优解的性能.在上述硬件与软件协同的解决方案下
架构的面积与计算效率得到了提升.实验数据显示
将本文的编译框架与最新技术比较
编译速度可提升1955倍
面积效率提升到1.36倍.
This paper provides a hardware and software co-design technique to optimize the compilation time and area efficiency of loop acceleration on Coarse-Grained Reconfigurable Architectures (CGRAs). From the hardware prospective
the architecture of each processing element (PE) is optimized by replacing register file within PE with bypass logic. On the software side
we develop a novel and efficient loop mapping algorithm
which greatly shrinks the search space for the optimal solution. The iteration of forward greedy placement and backward recovery obtains a fast and stable compile speed and guarantees performance close to the optimal solution.This hardware and software co-design method improves the area and computation efficiency. Experiment result shows that our framework improves1955xin compile speed and obtainsa 1.25x area efficiency.
LEVI G. A note on the derivation of maximal common subgraphs of two directed or undirected graphs[J]. Calcolo, 1973, 9(4): 341-352. DOI: 341
GAO M, KOZYRAKIS C. HRL: Efficient and flexible reconfigurable logic for near-data processing[C]//2016 IEEE International Symposium on High Performance Computer Architecture (HPCA). Barcelona: IEEE, 2016: 126-137.
RAU B R. Iterative module scheduling: An algorithm for software pipelining loops[C]//The 27th Annual IEEE/ACM International Symposium on Microarchitecture. San Jose: IEEE, 1994: 63-74.
ZHAO Z, SHENG W, JING N, et al. Resource-saving compile flow for coarse-grained reconfigurable architectures[C]//2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig). Cancun: IEEE, 2015: 1-8.
CHEN L, MITRA T. Graph minor approach for application mapping on CGRAs[C]//2012 International Conference on Field-Programmable Technology.Seoul: IEEE, 2012: 285-292.
EGGER B, LEE H, KANG D, et al. A space-and energy-efficient code compression/decompression technique for coarse-grained reconfigurable architectures[C]//2017 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). Austin: IEEE, 2017: 197-209.
HAMZEH M, SHRIVASTAVA A, VRUDHULA S. REGIMap: register-aware application mapping on coarse-grained reconfigurable architectures (CGRAs)[C]//Proceedings of the 50th Annual Design Automation Conference. Austin: ACM, 2013: 18.
CHEN Y H, EMER J, SZE V. Eyeriss: A spatial architecture for energy-efficient dataflow for convolutional neural networks[C]//2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA). Seoul: IEEE, 2016: 367-379.
HAMZEH M, SHRIVASTAVA A, VRUDHULA S. EPIMap: Using epimorphism to map applications on CGRAs[C]// DAC Design Automation Conference 2012. San Francisco: IEEE, 2012: 1280-1287.
DAVE S, BALASUBRAMANIAN M, SHRIVASTAVA A. RAMP: resource-aware mapping for CGRAs[C]//201855th ACM/ESDA/IEEE Design Automation Conference (DAC). San Francisco: IEEE, 2018: 1-6.
0
浏览量
19
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010602201714号