Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Shungo Kumazawa"'
Publikováno v:
IEEE Access, Vol 12, Pp 6926-6940 (2024)
Ensemble-based collaborative inference systems, Edge Ensembles, are deep learning edge inference systems that enhance accuracy by aggregating predictions from models deployed on each device. They offer several advantages, including scalability based
Externí odkaz:
https://doaj.org/article/25f3b7022f904ef1b16610bfb7796aa4
Autor:
Junnosuke Suzuki, Jaehoon Yu, Mari Yasunaga, Angel Lopez Garcia-Arias, Yasuyuki Okoshi, Shungo Kumazawa, Kota Ando, Kazushi Kawamura, Thiem Van Chu, Masato Motomura
Publikováno v:
IEEE Access, Vol 12, Pp 2057-2073 (2024)
With the widespread adoption of edge AI, the diversity of application requirements and fluctuating computational demands present significant challenges. Conventional accelerators suffer from increased memory footprints due to the need for multiple mo
Externí odkaz:
https://doaj.org/article/d7aa8884e28346a9bcfad7530e418021
Publikováno v:
International Journal of Networking and Computing. 11:215-230
Training machine learning models on edge devices is always a conflict with power consumption and computing cost. This paper introduces a hardware-oriented training method called ExtraFerns for a unique subset of decision tree ensembles, which signifi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1912708623631c092b1d92d6d25fb177
https://doi.org/10.15803/ijnc.11.2_215
https://doi.org/10.15803/ijnc.11.2_215