Zobrazeno 1 - 10
of 81
pro vyhledávání: '"Fu, Yonggan"'
Autor:
Wan, Zishen, Liu, Che-Kai, Yang, Hanchen, Raj, Ritik, Li, Chaojian, You, Haoran, Fu, Yonggan, Wan, Cheng, Li, Sixu, Kim, Youbin, Samajdar, Ananda, Lin, Yingyan Celine, Ibrahim, Mohamed, Rabaey, Jan M., Krishna, Tushar, Raychowdhury, Arijit
The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, are facing challenges surrounding unsustainable computational trajectories, limited robustness, and a lack of explainability. To develop next-gener
Externí odkaz:
http://arxiv.org/abs/2409.13153
Large Language Models (LLMs) have recently shown promise in streamlining hardware design processes by encapsulating vast amounts of domain-specific data. In addition, they allow users to interact with the design processes through natural language ins
Externí odkaz:
http://arxiv.org/abs/2407.01910
Attention is a fundamental component behind the remarkable achievements of large language models (LLMs). However, our current understanding of the attention mechanism, especially regarding how attention distributions are established, remains limited.
Externí odkaz:
http://arxiv.org/abs/2406.15765
Recent breakthroughs in Neural Radiance Fields (NeRFs) have sparked significant demand for their integration into real-world 3D applications. However, the varied functionalities required by different 3D applications often necessitate diverse NeRF mod
Externí odkaz:
http://arxiv.org/abs/2403.11131
Autor:
Wan, Zishen, Liu, Che-Kai, Yang, Hanchen, Li, Chaojian, You, Haoran, Fu, Yonggan, Wan, Cheng, Krishna, Tushar, Lin, Yingyan, Raychowdhury, Arijit
The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, have significantly impacted various aspects of our lives. However, the current challenges surrounding unsustainable computational trajectories, lim
Externí odkaz:
http://arxiv.org/abs/2401.01040
Boosting the task accuracy of tiny neural networks (TNNs) has become a fundamental challenge for enabling the deployments of TNNs on edge devices which are constrained by strict limitations in terms of memory, computation, bandwidth, and power supply
Externí odkaz:
http://arxiv.org/abs/2310.19820
Autor:
Fu, Yonggan, Zhang, Yongan, Yu, Zhongzhi, Li, Sixu, Ye, Zhifan, Li, Chaojian, Wan, Cheng, Lin, Yingyan
The remarkable capabilities and intricate nature of Artificial Intelligence (AI) have dramatically escalated the imperative for specialized AI accelerators. Nonetheless, designing these accelerators for various AI workloads remains both labor- and ti
Externí odkaz:
http://arxiv.org/abs/2309.10730
Despite the impressive performance recently achieved by automatic speech recognition (ASR), we observe two primary challenges that hinder its broader applications: (1) The difficulty of introducing scalability into the model to support more languages
Externí odkaz:
http://arxiv.org/abs/2306.15686
Tiny deep learning has attracted increasing attention driven by the substantial demand for deploying deep learning on numerous intelligent Internet-of-Things devices. However, it is still challenging to unleash tiny deep learning's full potential on
Externí odkaz:
http://arxiv.org/abs/2306.13586
Generalizable Neural Radiance Fields (GNeRF) are one of the most promising real-world solutions for novel view synthesis, thanks to their cross-scene generalization capability and thus the possibility of instant rendering on new scenes. While adversa
Externí odkaz:
http://arxiv.org/abs/2306.06359