DRIPS: Dynamic Rebalancing of Pipelined Streaming Applications on CGRAs

Conference · Sat Apr 02 04:00:00 EDT 2022

DOI:https://doi.org/10.1109/HPCA53966.2022.00030· OSTI ID:1877109

Tan, Cheng ^[1]; Bohm Agostini, Nicolas ^[1]; Geng, Tong ^[1]; Xie, Chenhao ^[1]; Li, Jiajia ^[1]; Li, Ang ^[1]; Barker, Kevin J. ^[1]; Tumeo, Antonino ^[1]

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1. BATTELLE (PACIFIC NW LAB)

Coarse-grained reconfigurable arrays (CGRAs) provide higher flexibility than application-specific integrated circuits (ASICs) and higher efficiency than fine-grained reconfigurable devices such as Field Programmable Gate Arrays (FPGAs). However, CGRAs are generally designed to support offloading of a single kernel. While their design, based on communicating functional units, appears to naturally suit data streaming applications composed of multiple cooperating kernels, current approaches only statically partition the resources across application kernels. However, emerging streaming applications at the edge (scientific instruments, sensor networks, network processing) perform much more than digital signal processing and often are data and input dependent. This leads to extremely variable kernel execution times, severely impacting the throughput of the entire pipeline if resources are only statically allocated. Therefore, in this paper, we propose DRIPS — a coarse-grained, dynamically, and partially reconfigurable array for data-dependent streaming applications. We present a unified compiler framework to facilitate the mapping of a given streaming application onto the DRIPS CGRA architecture. The experimental results show that DRIPS achieves an average throughput improvement of 1.46$$\times$$ across a set of representative applications over a statically partitioned solution. The additional area overhead to enable dynamic rebalancing consumes 16.34% of the entire area for a 5x5 CGRA prototype.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1877109
• Report Number(s): PNNL-SA-165149
• Country of Publication: United States
• Language: English