Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Angelo Garofalo"'
DARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training
Autor:
Angelo Garofalo, Yvan Tortorella, Matteo Perotti, Luca Valente, Alessandro Nadalini, Luca Benini, Davide Rossi, Francesco Conti
Publikováno v:
IEEE Open Journal of the Solid-State Circuits Society, Vol 2, Pp 231-243 (2022)
On-chip deep neural network (DNN) inference and training at the Extreme-Edge (TinyML) impose strict latency, throughput, accuracy, and flexibility requirements. Heterogeneous clusters are promising solutions to meet the challenge, combining the flexi
Externí odkaz:
https://doaj.org/article/6e8b4a55628e4539a6900c8db66fbba3
Autor:
Angelo Garofalo, Francesco Conti, DAVIDE ROSSI, Giuseppe Tagliavini, GIANMARCO OTTAVI, LUCA BENINI, Alfio Di Mauro
Publikováno v:
IEEE Transactions on Circuits and Systems I: Regular Papers, 70 (6)
Computationally intensive algorithms such as Deep Neural Networks (DNNs) are becoming killer applications for edge devices. Porting heavily data-parallel algorithms on resource-constrained and battery-powered devices poses several challenges related
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d0eb8a94b94aed3ec986823667594738
https://hdl.handle.net/20.500.11850/606661
https://hdl.handle.net/20.500.11850/606661
Autor:
Angelo Garofalo, Geethan Karunaratne, Francesco Conti, DAVIDE ROSSI, Irem Boybat, GIANMARCO OTTAVI, LUCA BENINI
Publikováno v:
IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 12:422-435
Deployment of modern TinyML tasks on small battery-constrained IoT devices requires high computational energy efficiency. Analog In-Memory Computing (IMC) using non-volatile memory (NVM) promises major efficiency improvements in deep neural network (
Autor:
Davide Rossi, Stefan Mach, Luca Benini, Simone Benatti, Angelo Garofalo, Fabio Montagna, Gianmarco Ottavi, Giuseppe Tagliavini
Publikováno v:
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems, 33 (5)
IEEE Transactions on Parallel and Distributed Systems, 33 (5)
Recent applications in low-power (1-20 mW) near-sensor computing require the adoption of floating-point arithmetic to reconcile high precision results with a wide dynamic range. In this article, we propose a low-power multi-core computing cluster tha
DARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training
Autor:
Angelo Garofalo, Yvan Tortorella, Matteo Perotti, Luca Valente, Alessandro Nadalini, Luca Benini, Davide Rossi, Francesco Conti
On-chip DNN inference and training at the Extreme-Edge (TinyML) impose strict latency, throughput, accuracy and flexibility requirements. Heterogeneous clusters are promising solutions to meet the challenge, combining the flexibility of DSP-enhanced
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0eecf21dab12a4e2f395397211e40225
Autor:
Angelo Garofalo, Matteo Perotti, Luca Valente, Yvan Tortorella, Alessandro Nadalini, Luca Benini, Davide Rossi, Francesco Conti
Publikováno v:
ESSCIRC 2022- IEEE 48th European Solid State Circuits Conference (ESSCIRC).
Publikováno v:
Proceedings of the 17th ACM International Conference on Computing Frontiers
CF
CF
The deployment of Quantized Neural Networks (QNN) on advanced microcontrollers requires optimized software to exploit digital signal processing (DSP) extensions of modern instruction set architectures (ISA). As such, recent research proposed optimize
Autor:
Angelo Garofalo, Giuseppe Tagliavini, Nazareno Bruschi, Alessio Burrello, Francesco Conti, Davide Rossi
Publikováno v:
IEEE Transactions on Computers
The deployment of Deep Neural Networks (DNNs) on end-nodes at the extreme edge of the Internet-of-Things is a critical enabler to support pervasive Deep Learning-enhanced applications. Low-Cost MCU-based end-nodes have limited on-chip memory and ofte
Publikováno v:
International Conference on Architecture of Computing Systems
Architecture of Computing Systems ISBN: 9783030816810
ARCS
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Architecture of Computing Systems
Architecture of Computing Systems ISBN: 9783030816810
ARCS
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Architecture of Computing Systems
High-level programming models aim at exploiting hardware parallelism and reducing software development costs. However, their adoption on ultra-low-power multi-core microcontroller (MCU) platforms requires minimizing the overheads of work-sharing cons
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aabe870fc525f9a4eed84fd98b8903aa
http://hdl.handle.net/11585/828853
http://hdl.handle.net/11585/828853
Autor:
Alfio Di Mauro, Luca Benini, Gianmarco Ottavi, Francesco Conti, Davide Rossi, Angelo Garofalo, Giuseppe Tagliavini
Publikováno v:
ESSCIRC 2021-IEEE 47th European Solid State Circuits Conference (ESSCIRC)
ESSCIRC
ESSCIRC
IoT end-nodes require extreme performance and energy efficiency coupled with high flexibility to deal with the increasing computational requirements and variety of modern near-sensor data analytics applications. Low-Bitwidth and Mixed-Precision arith
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::828bfac5c1ae1ded8371b5754db5305d
http://hdl.handle.net/11585/847035
http://hdl.handle.net/11585/847035