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20232026
most citedUnderstanding the Potential of FPGA-Based Spatial Acceleration for Large Language Model Inference

76 citations · 78 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2026

GauS: Differentiable Scheduling Optimization via Gaussian Reparameterization

Yaohui Cai, Vesal Bakhtazad, Cunxi Yu +1

Efficient operator scheduling is a fundamental challenge in software compilation and hardware synthesis. While recent differentiable approaches have sought to replace traditional o…

cs.AI2025

e-boost: Boosted E-Graph Extraction with Adaptive Heuristics and Exact Solving

Jiaqi Yin, Zhan Song, Chen Chen +3

E-graphs have attracted growing interest in many fields, particularly in logic synthesis and formal verification. E-graph extraction is a challenging NP-hard combinatorial optimiza…

cs.LG2025

HeuriGym: An Agentic Benchmark for LLM-Crafted Heuristics in Combinatorial Optimization

Hongzheng Chen, Yingheng Wang, Yaohui Cai +10

While Large Language Models (LLMs) have demonstrated significant advancements in reasoning and agent-based problem-solving, current evaluation methodologies fail to adequately asse…

cs.LG2024★ 2 cited

Trainable Fixed-Point Quantization for Deep Learning Acceleration on FPGAs

Dingyi Dai, Yichi Zhang, Jiahao Zhang +4

Quantization is a crucial technique for deploying deep learning models on resource-constrained devices, such as embedded FPGAs. Prior efforts mostly focus on quantizing matrix mult…

cs.LG2023★ 76 cited

Understanding the Potential of FPGA-Based Spatial Acceleration for Large Language Model Inference

Hongzheng Chen, Jiahao Zhang, Yixiao Du +5

Recent advancements in large language models (LLMs) boasting billions of parameters have generated a significant demand for efficient deployment in inference workloads. The majorit…