Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Kopetz, Tal"'
Autor:
Zandonati, Ben, Bucagu, Glenn, Pol, Adrian Alan, Pierini, Maurizio, Sirkin, Olya, Kopetz, Tal
Model compression is instrumental in optimizing deep neural network inference on resource-constrained hardware. The prevailing methods for network compression, namely quantization and pruning, have been shown to enhance efficiency at the cost of perf
Externí odkaz:
http://arxiv.org/abs/2302.07612
Model compression is vital to the deployment of deep learning on edge devices. Low precision representations, achieved via quantization of weights and activations, can reduce inference time and memory requirements. However, quantifying and predicting
Externí odkaz:
http://arxiv.org/abs/2210.08502
Autor:
Pol, Adrian Alan, Aarrestad, Thea, Govorkova, Ekaterina, Halily, Roi, Klempner, Anat, Kopetz, Tal, Loncar, Vladimir, Ngadiuba, Jennifer, Pierini, Maurizio, Sirkin, Olya, Summers, Sioni
We apply object detection techniques based on deep convolutional blocks to end-to-end jet identification and reconstruction tasks encountered at the CERN Large Hadron Collider (LHC). Collision events produced at the LHC and represented as an image co
Externí odkaz:
http://arxiv.org/abs/2202.04499
Autor:
Pol, Adrian Alan, Aarrestad, Thea, Govorkova, Katya, Halily, Roi, Klempner, Anat, Kopetz, Tal, Loncar, Vladimir, Ngadiuba, Jennifer, Pierini, Maurizio, Sirkin, Olya, Summers, Sioni
We apply object detection techniques based on Convolutional Neural Networks to jet reconstruction and identification at the CERN Large Hadron Collider. In particular, we focus on CaloJet reconstruction, representing each event as an image composed of
Externí odkaz:
http://arxiv.org/abs/2105.05785
In this paper we study the multiple access channel (MAC) with combined cooperation and partial cribbing and characterize its capacity region. Cooperation means that the two encoders send a message to one another via a rate-limited link prior to trans
Externí odkaz:
http://arxiv.org/abs/1404.0832
Quantized neural networks are well known for reducing latency, power consumption, and model size without significant degradation in accuracy, making them highly applicable for systems with limited resources and low power requirements. Mixed precision
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::814a9366fcac1aa4b5622a76bb19a6c5
http://arxiv.org/abs/2205.15437
http://arxiv.org/abs/2205.15437
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
2015 IEEE Energy Conversion Congress & Exposition (ECCE); 2015, p26-30, 5p
