3 citations · 4 across the 8 of their papers we have counts for
8 papers
From Craft to Kernel: A Governance-First Execution Architecture and Semantic ISA for Agentic Computers
Xiangyu Wen, Yuang Zhao, Xiaoyu Xu +9
The transition of agentic AI from brittle prototypes to production systems is stalled by a pervasive crisis of craft. We suggest that the prevailing orchestration paradigm-delegati…
CktEvo: Repository-Level RTL Code Benchmark for Design Evolution
Zhengyuan Shi, Jingxin Wang, Tairan Cheng +3
Register-Transfer Level (RTL) coding is an iterative, repository-scale process in which Power, Performance, and Area (PPA) emerge from interactions across many files and the downst…
From Craft to Constitution: A Governance-First Paradigm for Principled Agent Engineering
Qiang Xu, Xiangyu Wen, Changran Xu +2
The advent of powerful Large Language Models (LLMs) has ushered in an ``Age of the Agent,'' enabling autonomous systems to tackle complex goals. However, the transition from protot…
DeepRTL2: A Versatile Model for RTL-Related Tasks
Yi Liu, Hongji Zhang, Yunhao Zhou +3
The integration of large language models (LLMs) into electronic design automation (EDA) has significantly advanced the field, offering transformative benefits, particularly in regi…
Speculative Decoding for Verilog: Speed and Quality, All in One
Changran Xu, Yi Liu, Yunhao Zhou +3
The rapid advancement of large language models (LLMs) has revolutionized code generation tasks across various programming languages. However, the unique characteristics of programm…
DeepCircuitX: A Comprehensive Repository-Level Dataset for RTL Code Understanding, Generation, and PPA Analysis
Zeju Li, Changran Xu, Zhengyuan Shi +11
This paper introduces DeepCircuitX, a comprehensive repository-level dataset designed to advance RTL (Register Transfer Level) code understanding, generation, and power-performance…
DeepRTL: Bridging Verilog Understanding and Generation with a Unified Representation Model
Yi Liu, Changran Xu, Yunhao Zhou +2
Recent advancements in large language models (LLMs) have shown significant potential for automating hardware description language (HDL) code generation from high-level natural lang…
Evaluating Text-to-Image Generative Models: An Empirical Study on Human Image Synthesis
Muxi Chen, Yi Liu, Jian Yi +5
In this paper, we present an empirical study introducing a nuanced evaluation framework for text-to-image (T2I) generative models, applied to human image synthesis. Our framework c…