Runtime Energy Monitoring for RISC-V Soft-Cores
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915524882989056 |
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| author | Scionti, Alberto Savio, Paolo Lubrano, Francesco Terzo, Olivier Ferretti, Marco Apopei, Florin Bellucci, Juri Spano, Ennio Carriere, Luca |
| author_facet | Scionti, Alberto Savio, Paolo Lubrano, Francesco Terzo, Olivier Ferretti, Marco Apopei, Florin Bellucci, Juri Spano, Ennio Carriere, Luca |
| contents | Energy efficiency is one of the major concern in designing advanced computing infrastructures. From single nodes to large-scale systems (data centers), monitoring the energy consumption of the computing system when applications run is a critical task. Designers and application developers often rely on software tools and detailed architectural models to extract meaningful information and determine the system energy consumption. However, when a design space exploration is required, designers may incur in continuous tuning of the models to match with the system under evaluation. To overcome such limitations, we propose a holistic approach to monitor energy consumption at runtime without the need of running complex (micro-)architectural models. Our approach is based on a measurement board coupled with a FPGA-based System-on-Module. The measuring board captures currents and voltages (up to tens measuring points) driving the FPGA and exposes such values through a specific memory region. A running service reads and computes energy consumption statistics without consuming extra resources on the FPGA device. Our approach is also scalable to monitoring of multi-nodes infrastructures (clusters). We aim to leverage this framework to perform experiments in the context of an aeronautical design application; specifically, we will look at optimizing performance and energy consumption of a shallow artificial neural network on RISC-V based soft-cores. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26065 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Runtime Energy Monitoring for RISC-V Soft-Cores Scionti, Alberto Savio, Paolo Lubrano, Francesco Terzo, Olivier Ferretti, Marco Apopei, Florin Bellucci, Juri Spano, Ennio Carriere, Luca Hardware Architecture Energy efficiency is one of the major concern in designing advanced computing infrastructures. From single nodes to large-scale systems (data centers), monitoring the energy consumption of the computing system when applications run is a critical task. Designers and application developers often rely on software tools and detailed architectural models to extract meaningful information and determine the system energy consumption. However, when a design space exploration is required, designers may incur in continuous tuning of the models to match with the system under evaluation. To overcome such limitations, we propose a holistic approach to monitor energy consumption at runtime without the need of running complex (micro-)architectural models. Our approach is based on a measurement board coupled with a FPGA-based System-on-Module. The measuring board captures currents and voltages (up to tens measuring points) driving the FPGA and exposes such values through a specific memory region. A running service reads and computes energy consumption statistics without consuming extra resources on the FPGA device. Our approach is also scalable to monitoring of multi-nodes infrastructures (clusters). We aim to leverage this framework to perform experiments in the context of an aeronautical design application; specifically, we will look at optimizing performance and energy consumption of a shallow artificial neural network on RISC-V based soft-cores. |
| title | Runtime Energy Monitoring for RISC-V Soft-Cores |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2509.26065 |