9 citations · 30 across the 26 of their papers we have counts for
10 papers · 1 filter
CorrectBench: Automatic Testbench Generation with Functional Self-Correction using LLMs for HDL Design
Ruidi Qiu, Grace Li Zhang, Rolf Drechsler +2
Functional simulation is an essential step in digital hardware design. Recently, there has been a growing interest in leveraging Large Language Models (LLMs) for hardware testbench…
LLM-Aided Efficient Hardware Design Automation
Kangwei Xu, Ruidi Qiu, Zhuorui Zhao +3
With the rapidly increasing complexity of modern chips, hardware engineers are required to invest more effort in tasks such as circuit design, verification, and physical implementa…
An Efficient General-Purpose Optical Accelerator for Neural Networks
Sijie Fei, Amro Eldebiky, Grace Li Zhang +2
General-purpose optical accelerators (GOAs) have emerged as a promising platform to accelerate deep neural networks (DNNs) due to their low latency and energy consumption. Such an…
AutoBench: Automatic Testbench Generation and Evaluation Using LLMs for HDL Design
Ruidi Qiu, Grace Li Zhang, Rolf Drechsler +2
In digital circuit design, testbenches constitute the cornerstone of simulation-based hardware verification. Traditional methodologies for testbench generation during simulation-ba…
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…
Automated C/C++ Program Repair for High-Level Synthesis via Large Language Models
Kangwei Xu, Grace Li Zhang, Xunzhao Yin +3
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…
BasisN: Reprogramming-Free RRAM-Based In-Memory-Computing by Basis Combination for Deep Neural Networks
Amro Eldebiky, Grace Li Zhang, Xunzhao Yin +4
Deep neural networks (DNNs) have made breakthroughs in various fields including image recognition and language processing. DNNs execute hundreds of millions of multiply-and-accumul…
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…
LiveMind: Low-latency Large Language Models with Simultaneous Inference
Chuangtao Chen, Grace Li Zhang, Xunzhao Yin +3
In this paper, we introduce LiveMind, a novel low-latency inference framework for large language model (LLM) inference which enables LLMs to perform inferences with incomplete user…
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…