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Yuanlong Xiao

PROFILE

Yuanlong Xiao

Yilun Xiao developed advanced hardware acceleration features in the rapidstream-org/rapidstream-tapa repository, focusing on Xilinx AI Engine (AIE) integration and memory-mapped I/O support. Over three months, Yilun implemented end-to-end AIE target flows, including code generation and pipeline handling in C++ and Python, to streamline Vitis-based FPGA workflows. He introduced robust memory-mapped input/output types and incremental I/O APIs, enabling high-fidelity cosimulation and more reliable data flow for AIE-based designs. His work included refactoring for AIE port stream support and improving target attribute handling, demonstrating depth in compiler development, embedded systems, and hardware description language tooling for dependable system integration.

Overall Statistics

Feature vs Bugs

80%Features

Repository Contributions

13Total
Bugs
1
Commits
13
Features
4
Lines of code
1,570
Activity Months3

Your Network

8 people

Work History

March 2025

3 Commits • 1 Features

Mar 1, 2025

March 2025 monthly summary for rapidstream-tapa: Delivered AIE Ports I/O Stream Support and robustness improvements, focusing on enabling iostreams for AIE ports and hardening AIE target handling. These changes improved data flow reliability and reduced risk of undefined depths in AIE connections, enabling more dependable rapidstream-tapa designs.

January 2025

1 Commits • 1 Features

Jan 1, 2025

January 2025 — Delivered Memory-Mapped I/O Support for Cosimulation (immap/ommap) in rapidstream-org/rapidstream-tapa. Added memory-mapped input/output types with read_only, write_only, and read_write access, and generalized window_readincr/window_writeincr for incremental I/O to enable cosimulation workflows. The change is captured in commit 31bed31b94c220ec693327500f201b747b2500bc, laying the foundation for faster, higher-fidelity verification and better reuse of memory-mapped models across components.

December 2024

9 Commits • 2 Features

Dec 1, 2024

December 2024 focused on delivering end-to-end Xilinx AI Engine (AIE) target support in TAPA and enabling seamless integration with Vitis workflows. The work enhances RapidStream’s capabilities for AI accelerator workflows, improving developer throughput and hardware utilization.

Activity

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Quality Metrics

Correctness86.2%
Maintainability83.8%
Architecture86.2%
Performance75.4%
AI Usage23.0%

Skills & Technologies

Programming Languages

CC++PythonRST

Technical Skills

Build SystemsC++C++ MetaprogrammingC++ Template MetaprogrammingCode GenerationCode RefactoringCode TransformationCompiler DevelopmentDocumentationEmbedded SystemsEmbedded Systems DevelopmentFPGA DesignFPGA DevelopmentHardware AccelerationHardware Description Language

Repositories Contributed To

1 repo

Overview of all repositories you've contributed to across your timeline

rapidstream-org/rapidstream-tapa

Dec 2024 Mar 2025
3 Months active

Languages Used

CC++PythonRST

Technical Skills

Build SystemsC++C++ MetaprogrammingC++ Template MetaprogrammingCode GenerationCode Refactoring

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