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Exploiting Hardware-Based Data-Parallel and Multithreading Models for Smart Edge Computing in Reconfigurable FPGAs
- Source :
- IEEE Transactions on Computers. 71:2903-2914
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
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Abstract
- Current edge computing systems are deployed in highly complex application scenarios with dynamically changing requirements. In order to provide the expected performance and energy efficiency values in these situations, the use of heterogeneous hardware/software platforms at the edge has become widespread. However, these computing platforms still suffer from the lack of unified software-driven programming models to efficiently deploy multi-purpose hardware-accelerated solutions. In parallel, edge computing systems also face another huge challenge: operating under multiple conditions that were not taken into account during any of the design stages. Moreover, these conditions may change over time, forcing self-adaptation mechanisms to become a must. This paper presents an integrated architecture to exploit hardware-accelerated data-parallel models and transparent hardware/software multithreading. In particular, the proposed architecture leverages the \ARTICo framework and ReconOS to allow developers to select the most suitable programming model to deploy their edge computing applications onto run-time reconfigurable hardware devices. An evolvable hardware system is used as an additional architectural component during validation, providing support for continuous lifelong learning in smart edge computing scenarios. In particular, the proposed setup exhibits online learning capabilities that include learning by imitation from software-based reference algorithms.
- Subjects :
- business.industry
Data parallelism
Computer science
Reconfigurable computing
Theoretical Computer Science
Software
Computational Theory and Mathematics
Hardware and Architecture
Multithreading
Programming paradigm
Enhanced Data Rates for GSM Evolution
business
Evolvable hardware
Computer hardware
Edge computing
Subjects
Details
- ISSN :
- 23263814 and 00189340
- Volume :
- 71
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Computers
- Accession number :
- edsair.doi...........b238a6baa5038eb25afa70f0bf59f87b