Efficient Hardware Implementation of Incremental Learning and Inference on Chip
| Autor: | Nicolas Farrugia, Matthieu Arzel, Vincent Gripon, Ghouthi Boukli Hacene, Michel Jezequel |
|---|---|
| Přispěvatelé: | Département Electronique (IMT Atlantique - ELEC), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Lab-STICC_IMTA_CACS_IAS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL) |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
[INFO.INFO-GT]Computer Science [cs]/Computer Science and Game Theory [cs.GT] Artificial neural network business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Deep learning Quantization (signal processing) Computer Science - Computer Vision and Pattern Recognition Inference 02 engineering and technology Chip [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Computer Science::Hardware Architecture [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Transfer of learning Field-programmable gate array Classifier (UML) Computer hardware |
| Zdroj: | NEWCAS NEWCAS 2019 : 17th IEEE International New Circuits and Systems Conference NEWCAS 2019 : 17th IEEE International New Circuits and Systems Conference, Jun 2019, Munich, Germany. ⟨10.1109/NEWCAS44328.2019.8961310⟩ |
| DOI: | 10.1109/newcas44328.2019.8961310 |
| Popis: | In this paper, we tackle the problem of incrementally learning a classifier, one example at a time, directly on chip. To this end, we propose an efficient hardware implementation of a recently introduced incremental learning procedure that achieves state-of-the-art performance by combining transfer learning with majority votes and quantization techniques. The proposed design is able to accommodate for both new examples and new classes directly on the chip. We detail the hardware implementation of the method (implemented on FPGA target) and show it requires limited resources while providing a significant acceleration compared to using a CPU. In 2019 IEEE International NEWCAS Conference |
| Databáze: | OpenAIRE |
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