Zobrazeno 1 - 10
of 2 355
pro vyhledávání: '"A. Eshraghian"'
Autor:
Gunasekaran, Skye, Kembay, Assel, Ladret, Hugo, Zhu, Rui-Jie, Perrinet, Laurent, Kavehei, Omid, Eshraghian, Jason
Accurate time-series forecasting is essential across a multitude of scientific and industrial domains, yet deep learning models often struggle with challenges such as capturing long-term dependencies and adapting to drift in data distributions over t
Externí odkaz:
http://arxiv.org/abs/2410.15217
The rapid advancement of embedded multicore and many-core systems has revolutionized computing, enabling the development of high-performance, energy-efficient solutions for a wide range of applications. As models scale up in size, data movement is in
Externí odkaz:
http://arxiv.org/abs/2410.09650
Autor:
Chen, Zihao, Xiao, Zhili, Akl, Mahmoud, Leugring, Johannes, Olajide, Omowuyi, Malik, Adil, Dennler, Nik, Harper, Chad, Bose, Subhankar, Gonzalez, Hector A., Eshraghian, Jason, Pignari, Riccardo, Urgese, Gianvito, Andreou, Andreas G., Shankar, Sadasivan, Mayr, Christian, Cauwenberghs, Gert, Chakrabartty, Shantanu
We introduce NeuroSA, a neuromorphic architecture specifically designed to ensure asymptotic convergence to the ground state of an Ising problem using an annealing process that is governed by the physics of quantum mechanical tunneling using Fowler-N
Externí odkaz:
http://arxiv.org/abs/2406.05224
Autor:
Zhu, Rui-Jie, Zhang, Yu, Sifferman, Ethan, Sheaves, Tyler, Wang, Yiqiao, Richmond, Dustin, Zhou, Peng, Eshraghian, Jason K.
Matrix multiplication (MatMul) typically dominates the overall computational cost of large language models (LLMs). This cost only grows as LLMs scale to larger embedding dimensions and context lengths. In this work, we show that MatMul operations can
Externí odkaz:
http://arxiv.org/abs/2406.02528
Autonomous driving demands an integrated approach that encompasses perception, prediction, and planning, all while operating under strict energy constraints to enhance scalability and environmental sustainability. We present Spiking Autonomous Drivin
Externí odkaz:
http://arxiv.org/abs/2405.19687
Recent advancements in neuroscience research have propelled the development of Spiking Neural Networks (SNNs), which not only have the potential to further advance neuroscience research but also serve as an energy-efficient alternative to Artificial
Externí odkaz:
http://arxiv.org/abs/2405.13672
Weight quantization is used to deploy high-performance deep learning models on resource-limited hardware, enabling the use of low-precision integers for storage and computation. Spiking neural networks (SNNs) share the goal of enhancing efficiency, b
Externí odkaz:
http://arxiv.org/abs/2404.19668
Autor:
Schmidgall, Samuel, Harris, Carl, Essien, Ime, Olshvang, Daniel, Rahman, Tawsifur, Kim, Ji Woong, Ziaei, Rojin, Eshraghian, Jason, Abadir, Peter, Chellappa, Rama
There is increasing interest in the application large language models (LLMs) to the medical field, in part because of their impressive performance on medical exam questions. While promising, exam questions do not reflect the complexity of real patien
Externí odkaz:
http://arxiv.org/abs/2402.08113
Autor:
Pedersen, Jens E., Abreu, Steven, Jobst, Matthias, Lenz, Gregor, Fra, Vittorio, Bauer, Felix C., Muir, Dylan R., Zhou, Peng, Vogginger, Bernhard, Heckel, Kade, Urgese, Gianvito, Shankar, Sadasivan, Stewart, Terrence C., Sheik, Sadique, Eshraghian, Jason K.
Publikováno v:
Nat Commun 15, 8122 (2024)
Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neur
Externí odkaz:
http://arxiv.org/abs/2311.14641
Spiking Neural Networks (SNNs) have garnered substantial attention in brain-like computing for their biological fidelity and the capacity to execute energy-efficient spike-driven operations. As the demand for heightened performance in SNNs surges, th
Externí odkaz:
http://arxiv.org/abs/2311.06570