2 Layers below Xe-Diagram, one become confidential

I have finished a prototype of a beast application tool, but feel uncertain if I have the passion to keep improving that.

I have finalized the basic idea of project Xe-Diagram – aiming to show case a project that can visualize public architecture view. I am very satisfied on the current status. With sub pages reworked, reviewed and published, I think my project can sit better than the public Programmer’s Reference Manuals and can serve better as a knowledge base like MCP server for Intel post-si GPUs.

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Thoughts on Architecture-Awareness Performance Diagnosis

Today marks one year since the Intel GPA project was discontinued.

At the time, I felt a real sense of emptiness. GPA was an external-facing tool, and working on something used by people outside my immediate team gave me a strong feeling of purpose. It was not only a technical project to me; it also became part of my professional identity and even helped support my extraordinary ability visa case.

Over the past year, working in architecture modeling has changed how I think about GPU performance analysis. I started to look less at application behavior as a black box, and more at the command streams submitted to the GPU, the hardware blocks they touch, and the metrics that describe how those blocks behave. That perspective made me rethink what future GPU tuning workflows could look like.

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Xe-Diagram Page published (Finished!)

Glad to have published my vibe Xe-Diagram

Link: https://xwang186.com/xe-diagram/ (also can be accessed thru blog menu)

Architecture: Xe-HPG(A770)

Referred docs: Intel® Graphics for Linux* – Programmer’s Reference Manuals

(6/16/2026 update: seems the orginal link https://www.x.org/docs/intel/ACM/ — become unavailable now)

5/19/2026 – Finished! Glad to have included all my initial thoughts into the plugin!

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Happen to find the “shift” of ChatGPT answer

Today I had a surprisingly satisfying conversation with ChatGPT. I asked it to design a logic puzzle for me — something like an engineering-flavored investigation with access control, logs, time stamps, and suspicious statements. On the surface, it looked good: the framing was serious, the setup was detailed, and the tone suggested a real deduction problem.

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Revisit of my NVDLA adaption project in Novumind

Introduction to NVDLA

The NVIDIA Deep Learning Accelerator (NVDLA) is an open-source hardware project designed to accelerate deep learning inference tasks. Its flexible, scalable architecture enables developers to implement AI acceleration on various platforms, from FPGAs to ASICs. By offering a complete stack of hardware and software, NVDLA has become a cornerstone for many projects in the AI hardware space, making it an invaluable tool for prototyping and development.

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