1 citations · 1 across the 8 of their papers we have counts for
9 papers
Fengshui: Demystifying Chiplet Ecosystem and Bespoke Neural Network Accelerator Codesign
Haoran Jin, Jirong Yang, Zhiheng Zhang +5
Modern ML workloads, with stringent latency and energy constraints, are increasingly hard to run efficiently on homogeneous commodity hardware. We argue that operator-level disaggr…
Rethinking Agentic Kernel Generation for Emerging Accelerators
Ruijie Gao, Jirong Yang, Barry Lyu +2
Emerging accelerators often lack mature compiler backends, motivating neural agents that generate and repair kernels from architectural documentation and simulator feedback. This a…
Architectural Implications of Agentic AI Workflows
Jirong Yang, Peizhe Liu, Chaojie Zhang +1
Agentic AI is emerging in datacenters, but its architectural implications remain unexplored. We organize agentic workflows in a taxonomy and present its first architectural charact…
Agents' Last Exam
Yiyou Sun, Xinyang Han, Weichen Zhang +378
Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional d…
Aligning LLM agents with human learning and adjustment behavior: a dual agent approach
Tianming Liu, Jirong Yang, Yafeng Yin +3
Effective modeling of how human travelers learn and adjust their travel behavior from interacting with transportation systems is critical for system assessment and planning. Howeve…
Mozart: A Chiplet Ecosystem-Accelerator Codesign Framework for Composable Bespoke Application Specific Integrated Circuits
Haoran Jin, Jirong Yang, Yunpeng Liu +3
Modern AI acceleration faces a fundamental challenge: conventional assumptions about memory requirements, batching effectiveness, and latency-throughput tradeoffs are systemwide ge…
LiveOIBench: Can Large Language Models Outperform Human Contestants in Informatics Olympiads?
Kaijian Zou, Aaron Xiong, Yunxiang Zhang +6
Competitive programming problems are increasingly used to evaluate the coding capabilities of large language models (LLMs) due to their complexity and ease of verification. Yet, cu…
LLM-ABM for Transportation: Assessing the Potential of LLM Agents in System Analysis
Tianming Liu, Jirong Yang, Yafeng Yin
Agent-based modeling approaches represent the state-of-art in modeling travel demand and transportation system dynamics and are valuable tools for transportation planning. However,…
Toward LLM-Agent-Based Modeling of Transportation Systems: A Conceptual Framework
Tianming Liu, Jirong Yang, Yafeng Yin
In transportation system demand modeling and simulation, agent-based models and microsimulations are current state-of-the-art approaches. However, existing agent-based models still…