11 papers
LLM4RTL: Tool-Assisted LLM for RTL Generation
Jing Jin, Robert Chu, Ning Yan +1
Large language models (LLMs) have facilitated impressive progress in software engineering, code generation, tooling, and systems. Concurrently, a significant body of research has d…
Embodied Task Planning via Graph-Informed Action Generation with Large Language Models
Xiang Li, Ning Yan, Masood Mortazavi
While Large Language Models (LLMs) have demonstrated strong zero-shot reasoning capabilities, their deployment as embodied agents still faces fundamental challenges in long-horizon…
PULSE: Privileged Knowledge Transfer from Rich to Deployable Sensors for Embodied Multi-Sensory Learning
Zihan Zhao, Kaushik Pendiyala, Masood Mortazavi +1
Multi-sensory systems for embodied intelligence, from wearable body-sensor networks to instrumented robotic platforms, routinely face a sensor-asymmetry problem: the richest modali…
EvoMem: Improving Multi-Agent Planning with Dual-Evolving Memory
Wenzhe Fan, Ning Yan, Masood Mortazavi
Planning has been a cornerstone of artificial intelligence for solving complex problems, and recent progress in LLM-based multi-agent frameworks have begun to extend this capabilit…
CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design
Yifeng Xiao, Yurong Xu, Ning Yan +2
Simulation-based design space exploration (DSE) aims to efficiently optimize high-dimensional structured designs under complex constraints and expensive evaluation costs. Existing…
Inverse Design in Distributed Circuits Using Single-Step Reinforcement Learning
Jiayu Li, Masood Mortazavi, Ning Yan +2
The goal of inverse design in distributed circuits is to generate near-optimal designs that meet a desirable transfer function specification. Existing design exploration methods us…