Martin Chang
Systems software engineer working on HPC, GPGPU, and AI.
Beiträge
LLM 與相關技術為科技與人類帶來了巨大的轉變。但我們依然不知道 LLM 內部的原理與如何確保 LLM 不會有惡意行為。可解釋性技術提供了少數的切入點。與其存外部在訓練過程中控制語言模型的行為,不如直接打開模型,去直接探索甚至控制LLM的行為。
但這麼重要的技術卻鮮少被討論跟應用。這裡我們打開他的面紗,為未來控制更強大的 AI 爭取希望
Code: https://github.com/marty1885/llama.cpp/tree/rwkv-edit
Nekko open sourced the CORE-ET AI inference accelerator design based on Esperanto Technologies' ET-SOC-1 processor. This talk goes into how open sourcing RTL enables downstream development, any what's now possible only on open source hardware and the RISC-V open ISA
Source: https://github.com/openhwgroup/core-et
As AI workloads continue to expand beyond the cloud, developers are increasingly looking for open, efficient, and flexible platforms to run models at the edge and on personal devices. RISC-V, as an open instruction set architecture, is emerging as a promising foundation for this shift.
In this session, Martin will share practical engineering experiences from building AI-capable RISC-V systems — covering key areas such as CPU–accelerator integration, software stack enablement, and optimization for real-world workloads.
The talk will explore how Linux-based environments can support AI development on RISC-V today, what challenges remain across toolchains and runtimes, and how upstream collaboration is helping accelerate ecosystem maturity.
More info: https://events.canonical.com/event/146/contributions/952/