9 citations · 27 across the 20 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…