9 citations · 25 across the 18 of their papers we have counts for
7 papers · 1 filter
VClare: Resolving Imperfect Specifications in LLM-Based Verilog Generation
Zhuorui Zhao, Bing Li, Yu Li +2
Large language models (LLMs) have demonstrated promising capabilities in generating Verilog code from natural language specifications. However, human-written specifications often c…
VFocus: Better Verilog Generation from Large Language Model via Focused Reasoning
Zhuorui Zhao, Bing Li, Grace Li Zhang +1
Large Language Models (LLMs) have shown impressive potential in generating Verilog codes, but ensuring functional correctness remains a challenge. Existing approaches often rely on…
VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency
Zhuorui Zhao, Ruidi Qiu, Ing-Chao Lin +3
Large Language Models (LLMs) have demonstrated promising capabilities in generating Verilog code from module specifications. To improve the quality of such generated Verilog codes,…
Classification-Based Automatic HDL Code Generation Using LLMs
Wenhao Sun, Bing Li, Grace Li Zhang +3
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…
Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference
Christopher Wolters, Xiaoxuan Yang, Ulf Schlichtmann +1
Large language models (LLMs) have recently transformed natural language processing, enabling machines to generate human-like text and engage in meaningful conversations. This devel…
EncodingNet: A Novel Encoding-based MAC Design for Efficient Neural Network Acceleration
Bo Liu, Grace Li Zhang, Xunzhao Yin +2
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 c…