Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/33097
Title: | 3DRA: Dynamic Data-Driven Reconfigurable Architecture |
Authors: | Lee, Jinho Amornpaisannon, Burin Diavastos, Andreas Carlson, Trevor E. |
Major Field of Science: | Engineering and Technology |
Field Category: | Computer and Information Sciences |
Keywords: | CGRA;dynamic dataflow;Reconfigurable architectures;coarse-grained reconfigurable array;accelerators |
Issue Date: | 26-Sep-2023 |
Source: | IEEE Access , 2023, vol. 11, pp. 105288 - 105298 |
Volume: | 11 |
Start page: | 105288 |
End page: | 105298 |
Journal: | IEEE Access |
Abstract: | Specialized accelerators are becoming a standard way to achieve both high-performance and efficient computation. We see this trend extending to all areas of computing, from low-power edge-computing systems to high-performance processors in datacenters. Reconfigurable architectures, such as Coarse-Grained Reconfigurable Arrays (CGRAs), attempt to find a balance between performance and energy efficiency by trading off dynamism, flexibility, and programmability. Our goal in this work is to find a new solution that provides the flexibility of traditional CPUs, with the parallelism of a CGRA, to improve overall performance and energy efficiency. Our design, the Dynamic Data-Driven Reconfigurable Architecture (3DRA), is unique, in that it targets both low-latency and high-throughput workloads. This architecture implements a dynamic dataflow execution model that resolves data dependencies at run-time and utilizes non-blocking broadcast communication that reduces transmission latency to a single cycle to achieve high performance and energy efficiency. By employing a dynamic model, 3DRA eliminates costly mapping algorithms during compilation and improves the flexibility and compilation time of traditional CGRAs. The 3DRA architecture achieves up to 731MIPS/mW, and it improves performance by up to 4.43x compared to the current state-of-the-art CGRA-based accelerators. |
URI: | https://hdl.handle.net/20.500.14279/33097 |
ISSN: | 21693536 |
DOI: | 10.1109/ACCESS.2023.3319404 |
Type: | Article |
Affiliation : | National University of Singapore |
Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
Files in This Item:
File | Size | Format | |
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3DRA_Dynamic_Data-Driven_Reconfigurable_Architecture.pdf | 1.56 MB | Adobe PDF | View/Open |
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