"The best way to predict the future is to invent it."

— Alan Kay
Hong Chenchen

Hong Chenchen

洪晨辰

ByteDance · Shanghai, China

Focused on machine learning systems, LLM inference optimization, and compiler infrastructure. Building tools that bridge the gap between high-level ML frameworks and efficient hardware execution.

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About

I work at the intersection of machine learning systems and compiler infrastructure. My focus areas include LLM inference optimization, MLIR-based compilation pipelines, and building efficient bridges between high-level ML frameworks and domain-specific hardware. Currently at ByteDance, working on systems that make large-scale AI workloads run faster.

Interests

ML Systems LLM Inference MLIR LLVM Compiler Optimization PyTorch C/C++ Python Performance Engineering Domain-Specific Compilers

Publications

Tiling-Aware Vectorization Framework for Perfect Loop Nests in MLIR
ICA3PP 2025 CCF-C

Projects

MLIR Compilation Framework for FT-Matrix 2026

A production MLIR compiler targeting FT-Matrix with cost-model-driven optimization, PyTorch frontend, and a comprehensive benchmark framework achieving ~57x kernel speedup
CiteBot — Intelligent Citation Assistant 2026

An intelligent LaTeX citation assistant that automates reference discovery and BibTeX generation using LLM + NLP fusion

Writing

2026.03.10 CiteBot: Automating Academic Citations with LLM + NLP Fusion
2026.02.20 From PyTorch to MLIR: Building a TorchDynamo-Based Compiler Frontend
2026.02.05 Cost-Model-Driven Tiling in MLIR: Automating Vectorization Decisions
2026.01.20 Building a Production MLIR Compiler: Architecture and Design Decisions