7 papers
PLoRA: An NDP-Enhanced Pooled-Memory System for Cost-Efficient Multi-LoRA Serving
Zhongkai Yu, Ohm Rishabh Venkatachalam, Zheng Wang +9
Multi-LoRA serving is how one base model becomes thousands of specialized variants, one adapter per user, task, or agent, and the deployments can hold 1000-plus adapters. Serving t…
ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation
Zhongkai Yu, Yichen Lin, Chenyang Zhou +12
Existing API-based agentic systems for RTL code generation are fundamentally misaligned with industrial practice: they assume a golden testbench is available at generation time, re…
ChipBench: A Next-Step Benchmark for Evaluating LLM Performance in AI-Aided Chip Design
Zhongkai Yu, Chenyang Zhou, Yichen Lin +6
While Large Language Models (LLMs) show significant potential in hardware engineering, current benchmarks suffer from saturation and limited task diversity, failing to reflect LLMs…
Evolutionary Physics-Informed Temporal Fusion for Lane-Change Intention Prediction
Jiazhao Shi, Qiyang Xie, Ziyu Wang +7
Early lane-change intention prediction is essential for autonomous driving and ADAS, but it remains challenging because lane-changing behavior depends on evolving traffic risk, sur…
Towards Physics-informed Spatial Intelligence with Human Priors: An Autonomous Driving Pilot Study
Guanlin Wu, Boyan Su, Yang Zhao +3
How to integrate and verify spatial intelligence in foundation models remains an open challenge. Current practice often proxies Visual-Spatial Intelligence (VSI) with purely textua…
Multi-Scenario Highway Lane-Change Intention Prediction: A Physics-Informed AI Framework for Three-Class Classification
Jiazhao Shi, Yichen Lin, Yiheng Hua +6
Lane-change maneuvers are a leading cause of highway accidents, underscoring the need for accurate intention prediction to improve the safety and decision-making of autonomous driv…
ML-SceGen: A Multi-level Scenario Generation Framework
Yicheng Xiao, Yangyang Sun, Yicheng Lin
Current scientific research witnesses various attempts at applying Large Language Models for scenario generation but is inclined only to comprehensive or dangerous scenarios. In th…