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pro vyhledávání: '"P, Sarda"'
The capabilities of multi-antenna technology have recently been significantly enhanced by the proliferation of extra large array architectures. The high dimensionality of these systems implies that communications take place in the nearfield regime, w
Externí odkaz:
http://arxiv.org/abs/2410.19497
Autor:
Hamdi, Mohamed Amine, Daghero, Francesco, Sarda, Giuseppe Maria, Van Delm, Josse, Symons, Arne, Benini, Luca, Verhelst, Marian, Pagliari, Daniele Jahier, Burrello, Alessio
Streamlining the deployment of Deep Neural Networks (DNNs) on heterogeneous edge platforms, coupling within the same micro-controller unit (MCU) instruction processors and hardware accelerators for tensor computations, is becoming one of the crucial
Externí odkaz:
http://arxiv.org/abs/2410.08855
Generative AI is revolutionizing content creation and has the potential to enable real-time, personalized educational experiences. We investigated the effectiveness of converting textbook chapters into AI-generated podcasts and explored the impact of
Externí odkaz:
http://arxiv.org/abs/2409.04645
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
Publikováno v:
Clinical Ophthalmology, Vol Volume 18, Pp 3215-3226 (2024)
Sujata P Sarda,1 Guillaume Germain,2 Malena Mahendran,3 Jacob Klimek,3 Wendy Y Cheng,3 Roger Luo,1 Mei Sheng Duh3 1Apellis Pharmaceuticals, Waltham, MA, USA; 2Groupe d’analyse, Ltée, Montreal, Québec, Canada; 3Analysis Group, Inc, Boston, MA, USA
Externí odkaz:
https://doaj.org/article/5777f8456fab4119bc568cba5ad5cb8d
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