5 citations · 12 across the 17 of their papers we have counts for
18 papers
Learnable Low-Rank Polynomial Sketch for Effective Linear Attention
Zongyue Qin, Haoran Deng, Jason Cong +1
Softmax attention in Transformers suffers from quadratic complexity in sequence length, making it impractical for long-context applications. Linear attention alleviates this issue…
Can Agents Design Better Chips with a Higher Level Abstraction?
Zijian Ding, Yang Zou, Yizhou Sun +1
Large Language Model (LLM) agents are increasingly being explored for chip design, but most existing approaches operate directly at RTL. We ask whether agents can design better chi…
HarnessBridge: Learnable Bidirectional Controller for LLM Agent Harness
Xiaoxuan Wang, Haixin Wang, Alexander Taylor +3
Large language models are increasingly deployed as agents for long-horizon tasks, yet their performance is shaped not only by model capability and environment design, but also by t…
AgRefactor: Self-Evolving Agentic Workflow for HLS Compatibility and Performance
Yang Zou, Zijian Ding, Yizhou Sun +1
High-Level Synthesis (HLS) provides a fast path from concepts to silicon, but converting real-world software into synthesizable HLS code remains challenging due to restrictive lang…
AI+HW 2035: Shaping the Next Decade
Deming Chen, Jason Cong, Azalia Mirhoseini +27
Artificial intelligence (AI) and hardware (HW) are advancing at unprecedented rates, yet their trajectories have become inseparably intertwined. The global research community lacks…
Understand and Accelerate Memory Processing Pipeline for Large Language Model Inference
Zifan He, Rui Ma, Yizhou Sun +1
Modern large language models (LLMs) increasingly depends on efficient long-context processing and generation mechanisms, including sparse attention, retrieval-augmented generation…
ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning
Xiaoxuan Wang, Han Zhang, Haixin Wang +11
Agentic reinforcement learning (ARL) has rapidly gained attention as a promising paradigm for training agents to solve complex, multi-step interactive tasks. Despite encouraging ea…
FlexLLM: Composable HLS Library for Flexible Hybrid LLM Accelerator Design
Jiahao Zhang, Zifan He, Nicholas Fraser +3
We present FlexLLM, a composable High-Level Synthesis (HLS) library for rapid development of domain-specific LLM accelerators. FlexLLM exposes key architectural degrees of freedom…
Report for NSF Workshop on AI for Electronic Design Automation
Deming Chen, Vijay Ganesh, Weikai Li +7
This report distills the discussions and recommendations from the NSF Workshop on AI for Electronic Design Automation (EDA), held on December 10, 2024 in Vancouver alongside NeurIP…
Iceberg: Enhancing HLS Modeling with Synthetic Data
Zijian Ding, Tung Nguyen, Weikai Li +3
Deep learning-based prediction models for High-Level Synthesis (HLS) of hardware designs often struggle to generalize. In this paper, we study how to close the generalizability gap…
LLM-DSE: Searching Accelerator Parameters with LLM Agents
Hanyu Wang, Xinrui Wu, Zijian Ding +6
Even though high-level synthesis (HLS) tools mitigate the challenges of programming domain-specific accelerators (DSAs) by raising the abstraction level, optimizing hardware direct…
LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuning
Neha Prakriya, Zijian Ding, Yizhou Sun +1
FPGAs are increasingly adopted in datacenter environments for their reconfigurability and energy efficiency. High-Level Synthesis (HLS) tools have eased FPGA programming by raising…
Hierarchical Mixture of Experts: Generalizable Learning for High-Level Synthesis
Weikai Li, Ding Wang, Zijian Ding +4
High-level synthesis (HLS) is a widely used tool in designing Field Programmable Gate Array (FPGA). HLS enables FPGA design with software programming languages by compiling the sou…
Dynamic-Width Speculative Beam Decoding for Efficient LLM Inference
Zongyue Qin, Zifan He, Neha Prakriya +2
Large language models (LLMs) have shown outstanding performance across numerous real-world tasks. However, the autoregressive nature of these models makes the inference process slo…
Optimized Multi-Token Joint Decoding with Auxiliary Model for LLM Inference
Zongyue Qin, Ziniu Hu, Zifan He +3
Large language models (LLMs) have achieved remarkable success across diverse tasks, yet their inference processes are hindered by substantial time and energy demands due to single-…
HMT: Hierarchical Memory Transformer for Efficient Long Context Language Processing
Zifan He, Yingqi Cao, Zongyue Qin +3
Transformer-based large language models (LLM) have been widely used in language processing applications. However, due to the memory constraints of the devices, most of them restric…
ProgSG: Cross-Modality Representation Learning for Programs in Electronic Design Automation
Yunsheng Bai, Atefeh Sohrabizadeh, Zongyue Qin +3
Recent years have witnessed the growing popularity of domain-specific accelerators (DSAs), such as Google's TPUs, for accelerating various applications such as deep learning, searc…
Enabling Automated FPGA Accelerator Optimization Using Graph Neural Networks
Atefeh Sohrabizadeh, Yunsheng Bai, Yizhou Sun +1
High-level synthesis (HLS) has freed the computer architects from developing their designs in a very low-level language and needing to exactly specify how the data should be transf…