Zobrazeno 1 - 10
of 8 770
pro vyhledávání: '"Sarda A"'
Autor:
Shenoy MM, De A, Shah B, Das A, Saraswat A, Lahiri K, Yadav S, Sarda A, Chakraborty D, J D, Kamat S, Doshi Y, Gonsalves N, Choudhary A, Dhoot D, Mahadkar N, Bhushan S, Gadkari R, Barkate H
Publikováno v:
Drug Design, Development and Therapy, Vol Volume 17, Pp 2899-2908 (2023)
Manjunath M Shenoy,1 Abhishek De,2 Bela Shah,3 Anupam Das,4 Abir Saraswat,5 Koushik Lahiri,2 Sheetal Yadav,6 Aarti Sarda,2 Disha Chakraborty,7 Dharmender J,2 Shruti Kamat,2 Yashika Doshi,2 Nelry Gonsalves,2 Ankita Choudhary,2 Dhiraj Dhoot,8 Namrata M
Externí odkaz:
https://doaj.org/article/eaa38c21e14c493d9129566490d9798d
We prove that if an orientable 3-manifold $M$ admits a complete Riemannian metric whose scalar curvature is positive and has a subquadratic decay at infinity, then it decomposes as a (possibly infinite) connected sum of spherical manifolds and $\math
Externí odkaz:
http://arxiv.org/abs/2407.07198
Publikováno v:
2023 IEEE International Symposium on Workload Characterization (IISWC)
GPGPU execution analysis has always been tied to closed-source, proprietary benchmarking tools that provide high-level, non-exhaustive, and/or statistical information, preventing a thorough understanding of bottlenecks and optimization possibilities.
Externí odkaz:
http://arxiv.org/abs/2407.11999
Autor:
Van Delm, Josse, Vandersteegen, Maarten, Burrello, Alessio, Sarda, Giuseppe Maria, Conti, Francesco, Pagliari, Daniele Jahier, Benini, Luca, Verhelst, Marian
Publikováno v:
2023 60th ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, USA, 2023, pp. 1-6
Optimal deployment of deep neural networks (DNNs) on state-of-the-art Systems-on-Chips (SoCs) is crucial for tiny machine learning (TinyML) at the edge. The complexity of these SoCs makes deployment non-trivial, as they typically contain multiple het
Externí odkaz:
http://arxiv.org/abs/2406.07453
Autor:
T, Kevin Joshua, Agarwal, Arnav, Sanjay, Shriya, Sarda, Yash, Alex, John Sahaya Rani, Gupta, Saurav, Kumar, Sushant, Kamath, Vishwanath
Conversational systems are crucial for human-computer interaction, managing complex dialogues by identifying threads and prioritising responses. This is especially vital in multi-party conversations, where precise identification of threads and strate
Externí odkaz:
http://arxiv.org/abs/2403.05931
We present Prompt Cache, an approach for accelerating inference for large language models (LLM) by reusing attention states across different LLM prompts. Many input prompts have overlapping text segments, such as system messages, prompt templates, an
Externí odkaz:
http://arxiv.org/abs/2311.04934
Autor:
Risso, Matteo, Burrello, Alessio, Sarda, Giuseppe Maria, Benini, Luca, Macii, Enrico, Poncino, Massimo, Verhelst, Marian, Pagliari, Daniele Jahier
The need to execute Deep Neural Networks (DNNs) at low latency and low power at the edge has spurred the development of new heterogeneous Systems-on-Chips (SoCs) encapsulating a diverse set of hardware accelerators. How to optimally map a DNN onto su
Externí odkaz:
http://arxiv.org/abs/2306.05060
Autor:
Golovin, Daniel, Bartok, Gabor, Chen, Eric, Donahue, Emily, Huang, Tzu-Kuo, Kokiopoulou, Efi, Qin, Ruoyan, Sarda, Nikhil, Sybrandt, Justin, Tjeng, Vincent
In many software systems, heuristics are used to make decisions - such as cache eviction, task scheduling, and information presentation - that have a significant impact on overall system behavior. While machine learning may outperform these heuristic
Externí odkaz:
http://arxiv.org/abs/2304.13033
Autor:
Jebara Moaine, Belhabib Sofiane, Boillereaux Lionel, Havet Michel, Sarda Alain, Mousseau Pierre, Deterre Rémi
Publikováno v:
International Journal for Simulation and Multidisciplinary Design Optimization, Vol 8, p A12 (2017)
This work describes the implementation of a simple procedure that helps to easily position the heating elements in press plates used in high-temperature composites thermoforming process. The developed method permits to obtain desired temperature prof
Externí odkaz:
https://doaj.org/article/e9dcd49d0981402db6d4ac3e222e7d1f
Autor:
Bharadwaj, Meghana, Sarda, Sanjana
In this work, we demonstrate the viability of using federated learning to successfully predict energy consumption as well as solar production for all households within a certain network using low-power and low-space consuming embedded devices. We als
Externí odkaz:
http://arxiv.org/abs/2301.09165