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Pregled bibliografske jedinice broj: 525023

Implementation Framework for Artificial Neural Networks on FPGA


Škoda, Peter; Lipić, Tomislav; Srp, Ágoston; Medved Rogina, Branka; Skala, Karolj; Vajda, Ferenc
Implementation Framework for Artificial Neural Networks on FPGA // Proceedings Vol. I. MEET&GVS 34rd International Convention MIPRO 2011 / Biljanović, Petar ; Skala, Karolj (ur.).
Zagreb: Croatian Society for Information and Communication Technology, Electronics and Microelectronics - MIPRO, 2011. str. 274-278 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
Implementation Framework for Artificial Neural Networks on FPGA

Autori
Škoda, Peter ; Lipić, Tomislav ; Srp, Ágoston ; Medved Rogina, Branka ; Skala, Karolj ; Vajda, Ferenc

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings Vol. I. MEET&GVS 34rd International Convention MIPRO 2011 / Biljanović, Petar ; Skala, Karolj - Zagreb : Croatian Society for Information and Communication Technology, Electronics and Microelectronics - MIPRO, 2011, 274-278

Skup
34th International Convention on Information and Communication Technology, Electronics and Microelectronics

Mjesto i datum
Opatija, Hrvatska, 21-25. 05. 2011

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Field programmable gate array ; neural network

Sažetak
In an Artificial Neural Network (ANN) a large number of highly interconnected simple nonlinear processing units work in parallel to solve a specific problem. Parallelism, modularity and dynamic adaptation are three characteristics typically associated with ANNs. Field Programmable Gate Array (FPGA) based reconfigurable computing architectures are well suited to implement ANNs as one can exploit concurrency and rapidly reconfigure to adapt the weights and topologies of an ANN. ANNs are suitable for and widely used in various real-life applications. A large portion of these applications are realized as embedded computer systems. With continuous advancements in VLSI technology FPGAs have become more powerful and power efficient, enabling the FPGA implementation of ANNs in embedded systems. This paper proposes an FPGA ANN framework which facilitates implementation in embedded systems. A case study of an ANN implementation in an embedded fall detection system is presented to demonstrate the advantages of the proposed framework.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo



POVEZANOST RADA


Projekt / tema
098-0982560-2566 - Mjerenje i karakterizacija podataka iz stvarnog svijeta (Branka Medved-Rogina, )
098-0982562-2567 - Metode znanstvene vizualizacije (Karolj Skala, )

Ustanove
Institut "Ruđer Bošković", Zagreb