NeuroAdaptiveNet: A Reconfigurable FPGA-Based Neural Network System with Dynamic Model Selection
This paper presents NeuroAdaptiveNet, an FPGA-based neural network framework that dynamically self-adjusts its architectural configurations in real time to maximize performance across diverse datasets. The core innovation is a Dynamic Classifier Selection mechanism, which harnesses the k-Nearest Cen...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/2674-0729/4/2/24 |
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| author | Achraf El Bouazzaoui Omar Mouhib Abdelkader Hadjoudja |
| author_facet | Achraf El Bouazzaoui Omar Mouhib Abdelkader Hadjoudja |
| author_sort | Achraf El Bouazzaoui |
| collection | DOAJ |
| description | This paper presents NeuroAdaptiveNet, an FPGA-based neural network framework that dynamically self-adjusts its architectural configurations in real time to maximize performance across diverse datasets. The core innovation is a Dynamic Classifier Selection mechanism, which harnesses the k-Nearest Centroid algorithm to identify the most competent neural network model for each incoming data sample. By adaptively selecting the most suitable model configuration, NeuroAdaptiveNet achieves significantly improved classification accuracy and optimized resource usage compared to conventional, statically configured neural networks. Experimental results on four datasets demonstrate that NeuroAdaptiveNet can reduce FPGA resource utilization by as much as 52.85%, increase classification accuracy by 4.31%, and lower power consumption by up to 24.5%. These gains illustrate the clear advantage of real-time, per-input reconfiguration over static designs. These advantages are particularly crucial for edge computing and embedded applications, where computational constraints and energy efficiency are paramount. The ability of NeuroAdaptiveNet to tailor its neural network parameters and architecture on a per-input basis paves the way for more efficient and accurate AI solutions in resource-constrained environments. |
| format | Article |
| id | doaj-art-18e8bcf7d4864023b4b4c1329d4cb43c |
| institution | Kabale University |
| issn | 2674-0729 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Chips |
| spelling | doaj-art-18e8bcf7d4864023b4b4c1329d4cb43c2025-08-20T03:26:56ZengMDPI AGChips2674-07292025-05-01422410.3390/chips4020024NeuroAdaptiveNet: A Reconfigurable FPGA-Based Neural Network System with Dynamic Model SelectionAchraf El Bouazzaoui0Omar Mouhib1Abdelkader Hadjoudja2SETIME Laboratory, Faculty of Sciences, Ibn Tofail University, Kenitra 14000, MoroccoSETIME Laboratory, Faculty of Sciences, Ibn Tofail University, Kenitra 14000, MoroccoSETIME Laboratory, Faculty of Sciences, Ibn Tofail University, Kenitra 14000, MoroccoThis paper presents NeuroAdaptiveNet, an FPGA-based neural network framework that dynamically self-adjusts its architectural configurations in real time to maximize performance across diverse datasets. The core innovation is a Dynamic Classifier Selection mechanism, which harnesses the k-Nearest Centroid algorithm to identify the most competent neural network model for each incoming data sample. By adaptively selecting the most suitable model configuration, NeuroAdaptiveNet achieves significantly improved classification accuracy and optimized resource usage compared to conventional, statically configured neural networks. Experimental results on four datasets demonstrate that NeuroAdaptiveNet can reduce FPGA resource utilization by as much as 52.85%, increase classification accuracy by 4.31%, and lower power consumption by up to 24.5%. These gains illustrate the clear advantage of real-time, per-input reconfiguration over static designs. These advantages are particularly crucial for edge computing and embedded applications, where computational constraints and energy efficiency are paramount. The ability of NeuroAdaptiveNet to tailor its neural network parameters and architecture on a per-input basis paves the way for more efficient and accurate AI solutions in resource-constrained environments.https://www.mdpi.com/2674-0729/4/2/24FPGAneural networkdynamic classifier selectionneural network acceleratoradaptive accelerator |
| spellingShingle | Achraf El Bouazzaoui Omar Mouhib Abdelkader Hadjoudja NeuroAdaptiveNet: A Reconfigurable FPGA-Based Neural Network System with Dynamic Model Selection Chips FPGA neural network dynamic classifier selection neural network accelerator adaptive accelerator |
| title | NeuroAdaptiveNet: A Reconfigurable FPGA-Based Neural Network System with Dynamic Model Selection |
| title_full | NeuroAdaptiveNet: A Reconfigurable FPGA-Based Neural Network System with Dynamic Model Selection |
| title_fullStr | NeuroAdaptiveNet: A Reconfigurable FPGA-Based Neural Network System with Dynamic Model Selection |
| title_full_unstemmed | NeuroAdaptiveNet: A Reconfigurable FPGA-Based Neural Network System with Dynamic Model Selection |
| title_short | NeuroAdaptiveNet: A Reconfigurable FPGA-Based Neural Network System with Dynamic Model Selection |
| title_sort | neuroadaptivenet a reconfigurable fpga based neural network system with dynamic model selection |
| topic | FPGA neural network dynamic classifier selection neural network accelerator adaptive accelerator |
| url | https://www.mdpi.com/2674-0729/4/2/24 |
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