9 citations · 30 across the 26 of their papers we have counts for
26 papers
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…
LLM for EDA in Front-End Design: Challenges and Opportunities
Kangwei Xu, Bing Li, Ulf Schlichtmann
As chip complexity increases and time-to-market pressures grow, front-end design has become a critical bottleneck in chip development. Recently, Large Language Models (LLMs) have s…
CorrectHDL: Agentic HDL Design with LLMs Leveraging High-Level Synthesis as Reference
Kangwei Xu, Grace Li Zhang, Ulf Schlichtmann +1
Large Language Models (LLMs) have demonstrated remarkable potential in hardware front-end design using hardware description languages (HDLs). However, their inherent tendency towar…
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…
Large Language Models (LLMs) for Electronic Design Automation (EDA)
Kangwei Xu, Denis Schwachhofer, Jason Blocklove +10
With the growing complexity of modern integrated circuits, hardware engineers are required to devote more effort to the full design-to-manufacturing workflow. This workflow involve…
HLSTester: Efficient Testing of Behavioral Discrepancies with LLMs for High-Level Synthesis
Kangwei Xu, Bing Li, Grace Li Zhang +1
In high-level synthesis (HLS), C/C++ programs with synthesis directives are used to generate circuits for FPGA implementations. However, hardware-specific and platform-dependent ch…
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,…
Paradigm-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 face the hallucination proble…
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…
OplixNet: Towards Area-Efficient Optical Split-Complex Networks with Real-to-Complex Data Assignment and Knowledge Distillation
Ruidi Qiu, Amro Eldebiky, Grace Li Zhang +4
Having the potential for high speed, high throughput, and low energy cost, optical neural networks (ONNs) have emerged as a promising candidate for accelerating deep learning tasks…
Computational and Storage Efficient Quadratic Neurons for Deep Neural Networks
Chuangtao Chen, Grace Li Zhang, Xunzhao Yin +3
Deep neural networks (DNNs) have been widely deployed across diverse domains such as computer vision and natural language processing. However, the impressive accomplishments of DNN…
Fused Depthwise Tiling for Memory Optimization in TinyML Deep Neural Network Inference
Rafael Stahl, Daniel Mueller-Gritschneder, Ulf Schlichtmann
Memory optimization for deep neural network (DNN) inference gains high relevance with the emergence of TinyML, which refers to the deployment of DNN inference tasks on tiny, low-po…
PowerPruning: Selecting Weights and Activations for Power-Efficient Neural Network Acceleration
Richard Petri, Grace Li Zhang, Yiran Chen +2
Deep neural networks (DNNs) have been successfully applied in various fields. A major challenge of deploying DNNs, especially on edge devices, is power consumption, due to the larg…
Biologically Plausible Learning on Neuromorphic Hardware Architectures
Christopher Wolters, Brady Taylor, Edward Hanson +3
With an ever-growing number of parameters defining increasingly complex networks, Deep Learning has led to several breakthroughs surpassing human performance. As a result, data mov…
Class-based Quantization for Neural Networks
Wenhao Sun, Grace Li Zhang, Huaxi Gu +2
In deep neural networks (DNNs), there are a huge number of weights and multiply-and-accumulate (MAC) operations. Accordingly, it is challenging to apply DNNs on resource-constraine…