FastCGRA: A Modeling, Evaluation, and Exploration Platform for Large-Scale Coarse-Grained Reconfigurable Arrays
FastCGRA: A Modeling, Evaluation, and Exploration Platform for Large-Scale Coarse-Grained Reconfigurable Arrays
复制标题
FastCGRA：大规模粗粒度可重构阵列的建模、评估和探索平台
DOI：
--
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Xuegong Zhou
中科院分区：
文献类型：
--
作者：
Su Zheng;Kaisen Zhang;Yaoguang Tian;Wenbo Yin;Lingli Wang;Xuegong Zhou
粗粒度可重构阵列(CGRAs)在特定领域的应用中具有足够的灵活性和较高的硬件效率，这使得CGRAs适合于神经网络加速和边缘计算等快速发展的领域。为了满足快速演化的需求，我们提出了大规模CGRA的建模、映射和探测平台FastCGRA。FastCGRA支持分层体系结构描述和自动交换模块生成。连通性感知的打包和图划分算法旨在降低布局和布线的复杂性。FastCGRA中的图同态布局算法能够有效地在大规模CGRA上进行布局。打包和放置算法与基于协商的路由算法协作，形成完整的映射过程。FastCGRA可以支持大规模CGRA的建模和映射，比现有平台具有更高的布局和布线效率。本发明可以降低CGRA互连设计的复杂度。有了这些功能，FastCGRA可以推动大规模CGRA的探索。
Coarse-Grained Reconfigurable Arrays (CGRAs) provide sufficient flexibility in domain-specific applications with high hardware efficiency, which make CGRAs suitable for fast-evolving fields such as neural network acceleration and edge computing. To meet the requirement of the fast evolution, we propose FastCGRA, the modeling, mapping, and exploration platform for large-scale CGRAs. FastCGRA supports hierarchical architecture description and automatic switch module generation. Connectivity-aware packing and graph partition algorithms are designed to reduce the complexity of placement and routing. The graph homomorphism placement algorithm in FastCGRA enables efficient placement on large-scale CGRAs. The packing and placement algorithms cooperate with a negotiation-based routing algorithm to form an integral mapping procedure. FastCGRA can support the modeling and mapping of large-scale CGRAs with significantly higher placement and routing efficiency than existing platforms. The automatic switch module generation method can reduce the complexity of CGRA interconnection design. With these features, FastCGRA can boost the exploration of large-scale CGRAs.