Zobrazeno 1 - 10
of 118
pro vyhledávání: '"Yin, Xunzhao"'
The concept of Nash equilibrium (NE), pivotal within game theory, has garnered widespread attention across numerous industries. Recent advancements introduced several quantum Nash solvers aimed at identifying pure strategy NE solutions (i.e., binary
Externí odkaz:
http://arxiv.org/abs/2408.04169
While large language models (LLMs) have demonstrated the ability to generate hardware description language (HDL) code for digital circuits, they still suffer from the hallucination problem, which leads to the generation of incorrect HDL code or misun
Externí odkaz:
http://arxiv.org/abs/2407.18326
In High-Level Synthesis (HLS), converting a regular C/C++ program into its HLS-compatible counterpart (HLS-C) still requires tremendous manual effort. Various program scripts have been introduced to automate this process. But the resulting codes usua
Externí odkaz:
http://arxiv.org/abs/2407.03889
Autor:
Eldebiky, Amro, Zhang, Grace Li, Yin, Xunzhao, Zhuo, Cheng, Lin, Ing-Chao, Schlichtmann, Ulf, Li, Bing
Deep neural networks (DNNs) have made breakthroughs in various fields including image recognition and language processing. DNNs execute hundreds of millions of multiply-and-accumulate (MAC) operations. To efficiently accelerate such computations, ana
Externí odkaz:
http://arxiv.org/abs/2407.03738
In this paper, we introduce a novel low-latency inference framework for large language models (LLMs) inference which enables LLMs to perform inferences with incomplete prompts. By reallocating computational processes to prompt input phase, we achieve
Externí odkaz:
http://arxiv.org/abs/2406.14319
Deep neural networks (DNNs) have achieved great breakthroughs in many fields such as image classification and natural language processing. However, the execution of DNNs needs to conduct massive numbers of multiply-accumulate (MAC) operations on hard
Externí odkaz:
http://arxiv.org/abs/2402.18595
Autor:
Xu, Zhicheng, Liu, Che-Kai, Li, Chao, Mao, Ruibin, Yang, Jianyi, Kämpfe, Thomas, Imani, Mohsen, Li, Can, Zhuo, Cheng, Yin, Xunzhao
Rapid advancements in artificial intelligence have given rise to transformative models, profoundly impacting our lives. These models demand massive volumes of data to operate effectively, exacerbating the data-transfer bottleneck inherent in the conv
Externí odkaz:
http://arxiv.org/abs/2401.05708
Frequency multipliers, a class of essential electronic components, play a pivotal role in contemporary signal processing and communication systems. They serve as crucial building blocks for generating high-frequency signals by multiplying the frequen
Externí odkaz:
http://arxiv.org/abs/2312.17444
Compute-in-memory (CiM) is a promising solution for addressing the challenges of artificial intelligence (AI) and the Internet of Things (IoT) hardware such as 'memory wall' issue. Specifically, CiM employing nonvolatile memory (NVM) devices in a cro
Externí odkaz:
http://arxiv.org/abs/2312.17442
Autor:
Jiang, Mengnan, Wang, Jingcun, Eldebiky, Amro, Yin, Xunzhao, Zhuo, Cheng, Lin, Ing-Chao, Zhang, Grace Li
Deep neural networks (DNNs) have demonstrated remarkable success in various fields. However, the large number of floating-point operations (FLOPs) in DNNs poses challenges for their deployment in resource-constrained applications, e.g., edge devices.
Externí odkaz:
http://arxiv.org/abs/2312.05875