Author of the publication

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Network on RISC-V based IoT End Nodes.

A. Garofalo, G. Tagliavini, F. Conti, L. Benini, and D. Rossi. CoRR, (2020)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Davide Rossi

Davide Cadamuro

Davide Pennino

Davide Batic

Davide Pagani

Other publications of authors with the same name

YodaNN: An Architecture for Ultra-Low Power Binary-Weight CNN AccelerationR. Andri, L. Cavigelli, D. Rossi, and L. Benini. (2016)cite arxiv:1606.05487.A Fully Programmable eFPGA-Augmented SoC for Smart Power Applications.F. Renzini, C. Mucci, D. Rossi, E. Scarselli, and R. Canegallo. IEEE Trans. Circuits Syst. I Regul. Pap., 67-I (2): 489-501 (2020)Always-On 674uW @ 4GOP/s Error Resilient Binary Neural Networks with Aggressive SRAM Voltage Scaling on a 22nm IoT End-Node.A. Mauro, F. Conti, P. Schiavone, D. Rossi, and L. Benini. CoRR, (2020)Energy-Efficient Hardware-Accelerated Synchronization for Shared-L1-Memory Multiprocessor Clusters.F. Glaser, G. Tagliavini, D. Rossi, G. Haugou, Q. Huang, and L. Benini. CoRR, (2020)μDMA: An autonomous I/O subsystem for IoT end-nodes.A. Pullini, D. Rossi, G. Haugou, and L. Benini. PATMOS, page 1-8. IEEE, (2017)Scalable EEG seizure detection on an ultra low power multi-core architecture.S. Benatti, F. Montagna, D. Rossi, and L. Benini. BioCAS, page 86-89. IEEE, (2016)A 0.5GHz 0.35mW LDO-Powered Constant-Slope Phase Interpolator With 0.22% INL.A. Elnaqib, H. Okuhara, T. Jang, D. Rossi, and L. Benini. IEEE Trans. Circuits Syst. II Express Briefs, 68 (1): 156-160 (2021)Energy Efficiency of Opportunistic Refreshing for Gain-Cell Embedded DRAM.B. Frankel, E. Sarfati, D. Rossi, and S. Wimer. IEEE Trans. Circuits Syst. I Regul. Pap., 70 (4): 1605-1612 (April 2023)HULK-V: a Heterogeneous Ultra-low-power Linux capable RISC-V SoC.L. Valente, Y. Tortorella, M. Sinigaglia, G. Tagliavini, A. Capotondi, L. Benini, and D. Rossi. CoRR, (2022)Marsellus: A Heterogeneous RISC-V AI-IoT End-Node SoC with 2-to-8b DNN Acceleration and 30%-Boost Adaptive Body Biasing.F. Conti, G. Paulin, D. Rossi, A. Mauro, G. Rutishauser, G. Ottavi, M. Eggimann, H. Okuhara, and L. Benini. CoRR, (2023)

BibSonomy

Disambiguation of "Rossi, Davide"

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Network on RISC-V based IoT End Nodes.

Please choose a person to relate this publication to

Davide Rossi

Davide Cadamuro

Davide Pennino

Davide Batic

Davide Pagani

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Rossi, Davide"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Network on RISC-V based IoT End Nodes.

Please choose a person to relate this publication to

Davide Rossi

Davide Cadamuro

Davide Pennino

Davide Batic

Davide Pagani

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Network on RISC-V based IoT End Nodes.