Zobrazeno 1 - 10
of 3 493
pro vyhledávání: '"Mendis, P."'
Autor:
Chen, Deming, Youssef, Alaa, Pendse, Ruchi, Schleife, André, Clark, Bryan K., Hamann, Hendrik, He, Jingrui, Laino, Teodoro, Varshney, Lav, Wang, Yuxiong, Sil, Avirup, Jabbarvand, Reyhaneh, Xu, Tianyin, Kindratenko, Volodymyr, Costa, Carlos, Adve, Sarita, Mendis, Charith, Zhang, Minjia, Núñez-Corrales, Santiago, Ganti, Raghu, Srivatsa, Mudhakar, Kim, Nam Sung, Torrellas, Josep, Huang, Jian, Seelam, Seetharami, Nahrstedt, Klara, Abdelzaher, Tarek, Eilam, Tamar, Zhao, Huimin, Manica, Matteo, Iyer, Ravishankar, Hirzel, Martin, Adve, Vikram, Marinov, Darko, Franke, Hubertus, Tong, Hanghang, Ainsworth, Elizabeth, Zhao, Han, Vasisht, Deepak, Do, Minh, Oliveira, Fabio, Pacifici, Giovanni, Puri, Ruchir, Nagpurkar, Priya
This white paper, developed through close collaboration between IBM Research and UIUC researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads through innovative, full-stack co
Externí odkaz:
http://arxiv.org/abs/2411.13239
Autor:
Kepaptsoglou, Demie, Castellanos-Reyes, José Ángel, Kerrigan, Adam, Nascimento, Júlio Alves Do, Zeiger, Paul M., Hajraoui, Khalil El, Idrobo, Juan Carlos, Mendis, Budhika G., Bergman, Anders, Lazarov, Vlado K., Rusz, Ján, Ramasse, Quentin M.
The miniaturisation of transistors is approaching its limits due to challenges in heat management and information transfer speed. To overcome these obstacles, emerging technologies such as spintronics are being developed, which leverage the electron'
Externí odkaz:
http://arxiv.org/abs/2410.02908
Autor:
Gupta, Ahan, Yuan, Yueming, Jain, Devansh, Ge, Yuhao, Aponte, David, Zhou, Yanqi, Mendis, Charith
Multi-head-self-attention (MHSA) mechanisms achieve state-of-the-art (SOTA) performance across natural language processing and vision tasks. However, their quadratic dependence on sequence lengths has bottlenecked inference speeds. To circumvent this
Externí odkaz:
http://arxiv.org/abs/2407.16847
Autor:
Wijayaraja, Jaliya L., Wijekoon, Janaka L., Wijesundara, Malitha, Wickramasinghe, L. J. Mendis
The long-distance detection of the presence of elephants is pivotal to addressing the human-elephant conflict. IoT-based solutions utilizing seismic signals originating from the movement of elephants are a novel approach to solving this problem. This
Externí odkaz:
http://arxiv.org/abs/2406.05140
We develop a declarative DSL - \cf - that can be used to specify Abstract Interpretation-based DNN certifiers. In \cf, programmers can easily define various existing and new abstract domains and transformers, all within just a few 10s of Lines of Cod
Externí odkaz:
http://arxiv.org/abs/2403.18729
Autor:
Mandhati, Sriram Reddy, Deshapriya, N. Lakmal, Mendis, Chatura Lavanga, Gunasekara, Kavinda, Yrle, Frank, Chaksan, Angsana, Sanjeev, Sujit
Plastic pollution is a critical environmental issue, and detecting and monitoring plastic litter is crucial to mitigate its impact. This paper presents the methodology of mapping street-level litter, focusing primarily on plastic waste and the locati
Externí odkaz:
http://arxiv.org/abs/2401.14719
Autor:
Phothilimthana, Phitchaya Mangpo, Abu-El-Haija, Sami, Cao, Kaidi, Fatemi, Bahare, Burrows, Mike, Mendis, Charith, Perozzi, Bryan
Precise hardware performance models play a crucial role in code optimizations. They can assist compilers in making heuristic decisions or aid autotuners in identifying the optimal configuration for a given program. For example, the autotuner for XLA,
Externí odkaz:
http://arxiv.org/abs/2308.13490
Many efficient $\textit{approximate}$ self-attention techniques have become prevalent since the inception of the transformer architecture. Two popular classes of these techniques are low-rank and kernel methods. Each of these methods has its strength
Externí odkaz:
http://arxiv.org/abs/2306.15799
Autor:
Lenadora, Damitha, Sathia, Vimarsh, Gerogiannis, Gerasimos, Yesil, Serif, Torrellas, Josep, Mendis, Charith
Over the years, many frameworks and optimization techniques have been proposed to accelerate graph neural networks (GNNs). Compared to the optimizations explored in these systems, we observe that different matrix re-associations of GNN computations l
Externí odkaz:
http://arxiv.org/abs/2306.15155
Autor:
Cao, Kaidi, Phothilimthana, Phitchaya Mangpo, Abu-El-Haija, Sami, Zelle, Dustin, Zhou, Yanqi, Mendis, Charith, Leskovec, Jure, Perozzi, Bryan
Learning to predict properties of large graphs is challenging because each prediction requires the knowledge of an entire graph, while the amount of memory available during training is bounded. Here we propose Graph Segment Training (GST), a general
Externí odkaz:
http://arxiv.org/abs/2305.12322