Spatiotemporal Strategies for Long-Term FPGA Resource Management

Mehrabi, Atefeh; Sorin, Daniel J.; Lee, Benjamin C.

doi:10.1109/ispass55109.2022.00026

Cited by 2 publications

(1 citation statement)

References 62 publications

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“…By selecting the most appropriate distribution that best enhances the temporal computation density based on runtime workload demands, the system's throughput is improved. Mehrabi [22] highlighted the importance of considering spatio-temporal strategies in FPGA resource management to achieve long-term target allocations and optimize FPGA usage efficiency by tracking and correcting deviations.…”

Section: Related Workmentioning

confidence: 99%

GLRM: Geometric Layout-Based Resource Management Method on Multiple Field Programmable Gate Array Systems

Gao,

Li,

Zhou

et al. 2024

Electronics

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Multiple field programmable gate array (Multi-FPGA) systems are capable of forming larger and more powerful computing units through high-speed interconnections between chips and are beginning to be widely used by various computing service providers. However, the new computing architecture brings new challenges to the system’s task resource management. Existing resource management methods do not fully exploit resources in Multi-FPGA systems, and it is difficult to support fast resource request and release. In this regard, we propose a geometric layout-based resource management (GLRM) method for Multi-FPGA systems. First, a geometric layout-based task combination algorithm (TCA) was proposed to ensure that the final system can use the available FPGA resources more efficiently. Then, we optimised two resource management algorithms using TCA. Compared with state-of-the-art resource management methods, TCA increases resource flexibility by an average of 6% and resource utilisation by an average of 7%, and the two optimised resource management methods are effective in improving resource management performance.

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Section: Related Workmentioning

confidence: 99%

GLRM: Geometric Layout-Based Resource Management Method on Multiple Field Programmable Gate Array Systems

Gao,

Li,

Zhou

et al. 2024

Electronics

View full text Add to dashboard Cite

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Flexible Updating of Internet of Things Computing Functions through Optimizing Dynamic Partial Reconfiguration

Kornaros,

Leivadaros,

Kolimbianakis

2024

ACM Trans. Embed. Comput. Syst.

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With applications to become increasingly compute- and data-intensive requiring more processing power, many internet-of-things (IoT) platforms in robots, drones, and autonomous vehicles which implement neural network inference, cryptographic functions or signal processing (e.g., multimedia, communication), employ field programmable gate arrays (FPGAs). At the same time, dynamic partial reconfiguration (DPR) in modern FPGAs enable changing the function of a part of the FPGA by dynamically loading new bitstreams to the logic regions without affecting the function of other parts of the FPGA. This is especially useful, to update functions of IoT devices while in operation, for bug fixing or functionality adjustments, and more importantly when these IoT devices integrate low-cost FPGAs that can hardly realize many hard accelerators. To deal with one of the major limitations of using partial reconfiguration in IoT devices, this work introduces techniques to flexibly use DPR, namely FLEXDPR, by sharing reconfigurable partitions among different accelerator functions and by supporting virtual relocation of these functions. Experimental results on the Xilinx ZYNQ-7000 platform reveal energy and latency efficiency improvements of, on average, about 20%. Overall, the suggested approach can reduce partial reconfiguration overhead while easing the scheduler’s decisions for the deployment of hardware functions throughout time and space in a performance-conscious manner.

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Spatiotemporal Strategies for Long-Term FPGA Resource Management

Cited by 2 publications

References 62 publications

GLRM: Geometric Layout-Based Resource Management Method on Multiple Field Programmable Gate Array Systems

GLRM: Geometric Layout-Based Resource Management Method on Multiple Field Programmable Gate Array Systems

Flexible Updating of Internet of Things Computing Functions through Optimizing Dynamic Partial Reconfiguration

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