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11.3 Metis AIPU: A 12nm 15TOPS/W 209.6TOPS SoC for Cost- and Energy-Efficient Inference at the Edge.

P. Hager, B. Moons, S. Cosemans, I. Papistas, B. Rooseleer, J. Loon, R. Uytterhoeven, F. Zaruba, S. Koumousi, M. Stanisavljevic, S. Mach, S. Mutsaards, R. Aljameh, G. Khov, B. Machiels, C. Olar, A. Psarras, S. Geursen, J. Vermeeren, Y. Lu, A. Maringanti, D. Ameta, L. Katselas, N. Hütter, M. Schmuck, S. Sivadas, K. Sharma, M. Oliveira, R. Aerne, N. Sharma, T. Soni, B. Bussolino, D. Pesut, M. Pallaro, A. Podlesnii, A. Lyrakis, Y. Ruffiner, M. Dazzi, J. Thiele, K. Goetschalckx, N. Bruschi, J. Doevenspeck, B. Verhoef, S. Linz, G. Garcea, J. Ferguson, I. Koltsidas, and E. Eleftheriou. ISSCC, page 212-214. IEEE, (2024)

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Beatrice Henk

Beatrice Klaile

Beatrice Gehrmann

Beatrice Brunhöber

Béatrice Lienemann

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NLCMAP: A Framework for the Efficient Mapping of Non-Linear Convolutional Neural Networks on FPGA Accelerators.G. Aiello, B. Bussolino, E. Valpreda, M. Roch, G. Masera, M. Martina, and S. Marsi. ICIP, page 926-930. IEEE, (2022)Enabling Capsule Networks at the Edge through Approximate Softmax and Squash Operations.A. Marchisio, B. Bussolino, E. Salvati, M. Martina, G. Masera, and M. Shafique. ISLPED, page 27:1-27:6. ACM, (2022)Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tile.R. Andri, B. Bussolino, A. Cipolletta, L. Cavigelli, and Z. Wang. CoRR, (2022)NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks.A. Marchisio, A. Massa, V. Mrazek, B. Bussolino, M. Martina, and M. Shafique. ICCAD, page 114:1-114:9. IEEE, (2020)FasTrCaps: An Integrated Framework for Fast yet Accurate Training of Capsule Networks.A. Marchisio, B. Bussolino, A. Colucci, M. Hanif, M. Martina, G. Masera, and M. Shafique. IJCNN, page 1-8. IEEE, (2020)RoHNAS: A Neural Architecture Search Framework With Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks.A. Marchisio, V. Mrazek, A. Massa, B. Bussolino, M. Martina, and M. Shafique. IEEE Access, (2022)X-TrainCaps: Accelerated Training of Capsule Nets through Lightweight Software Optimizations.A. Marchisio, B. Bussolino, A. Colucci, M. Hanif, M. Martina, G. Masera, and M. Shafique. CoRR, (2019)Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tiles.R. Andri, B. Bussolino, A. Cipolletta, L. Cavigelli, and Z. Wang. MICRO, page 582-598. IEEE, (2022)11.3 Metis AIPU: A 12nm 15TOPS/W 209.6TOPS SoC for Cost- and Energy-Efficient Inference at the Edge.P. Hager, B. Moons, S. Cosemans, I. Papistas, B. Rooseleer, J. Loon, R. Uytterhoeven, F. Zaruba, S. Koumousi, M. Stanisavljevic and 38 other author(s). ISSCC, page 212-214. IEEE, (2024)Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead.M. Capra, B. Bussolino, A. Marchisio, G. Masera, M. Martina, and M. Shafique. CoRR, (2020)

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Disambiguation of "Bussolino, Beatrice"

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11.3 Metis AIPU: A 12nm 15TOPS/W 209.6TOPS SoC for Cost- and Energy-Efficient Inference at the Edge.

Please choose a person to relate this publication to

Beatrice Henk

Beatrice Klaile

Beatrice Gehrmann

Beatrice Brunhöber

Béatrice Lienemann

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Bussolino, Beatrice"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML 11.3 Metis AIPU: A 12nm 15TOPS/W 209.6TOPS SoC for Cost- and Energy-Efficient Inference at the Edge.

Please choose a person to relate this publication to

Beatrice Henk

Beatrice Klaile

Beatrice Gehrmann

Beatrice Brunhöber

Béatrice Lienemann

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
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11.3 Metis AIPU: A 12nm 15TOPS/W 209.6TOPS SoC for Cost- and Energy-Efficient Inference at the Edge.