Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Huang, Sitao"'
Recent innovations in Transformer-based large language models have significantly advanced the field of general-purpose neural language understanding and generation. With billions of trainable parameters, deployment of these large models relies on hig
Externí odkaz:
http://arxiv.org/abs/2410.07407
High-level synthesis (HLS) allows hardware designers to create hardware designs with high-level programming languages like C/C++/OpenCL, which greatly improves hardware design productivity. However, existing HLS flows require programmers' hardware de
Externí odkaz:
http://arxiv.org/abs/2410.07356
Neural Architecture Search (NAS) has proven effective in discovering new Convolutional Neural Network (CNN) architectures, particularly for scenarios with well-defined accuracy optimization goals. However, previous approaches often involve time-consu
Externí odkaz:
http://arxiv.org/abs/2408.15034
Autor:
Wu, Kun, Park, Jeongmin Brian, Zhang, Xiaofan, Hidayetoğlu, Mert, Mailthody, Vikram Sharma, Huang, Sitao, Lumetta, Steven Sam, Hwu, Wen-mei
The growth rate of the GPU memory capacity has not been able to keep up with that of the size of large language models (LLMs), hindering the model training process. In particular, activations -- the intermediate tensors produced during forward propag
Externí odkaz:
http://arxiv.org/abs/2408.10013
Images captured under low-light scenarios often suffer from low quality. Previous CNN-based deep learning methods often involve using Retinex theory. Nevertheless, most of them cannot perform well in more complicated datasets like LOL-v2 while consum
Externí odkaz:
http://arxiv.org/abs/2406.09656
Neural architecture search (NAS) is an effective method for discovering new convolutional neural network (CNN) architectures. However, existing approaches often require time-consuming training or intensive sampling and evaluations. Zero-shot NAS aims
Externí odkaz:
http://arxiv.org/abs/2404.00271
Autor:
Zhang, Yifan, Malawade, Arnav Vaibhav, Zhang, Xiaofang, Li, Yuhui, Seong, DongHwan, Faruque, Mohammad Abdullah Al, Huang, Sitao
Autonomous systems (AS) are systems that can adapt and change their behavior in response to unanticipated events and include systems such as aerial drones, autonomous vehicles, and ground/aquatic robots. AS require a wide array of sensors, deep-learn
Externí odkaz:
http://arxiv.org/abs/2306.15748
Publikováno v:
The 60th Annual Design Automation Conference (DAC), 2023
Brain-inspired hyperdimensional computing (HDC) has been recently considered a promising learning approach for resource-constrained devices. However, existing approaches use static encoders that are never updated during the learning process. Conseque
Externí odkaz:
http://arxiv.org/abs/2304.05503
Publikováno v:
The 60th Annual Design Automation Conference (DAC), 2023
Cybersecurity has emerged as a critical challenge for the industry. With the large complexity of the security landscape, sophisticated and costly deep learning models often fail to provide timely detection of cyber threats on edge devices. Brain-insp
Externí odkaz:
http://arxiv.org/abs/2304.06728
Relational graph neural networks (RGNNs) are graph neural networks with dedicated structures for modeling the different types of nodes and edges in heterogeneous graphs. While RGNNs have been increasingly adopted in many real-world applications due t
Externí odkaz:
http://arxiv.org/abs/2301.06284