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进击のOS 杭州电子科技大学

目前在RISC-V平台的OS还较少，支持MMU的OS也比较少，因此本项目以支持MMU虚拟内存机制的RT-Thread Smart（简称rt-smart）为基础，在qemu上以及K210上实现运行。

山东大学 using the xv6-k210 (https://github.com/HUST-OS/xv6-k210)

专为 openEuler 设计的轻量级系统监控工具

复旦大学，T202510246998107，fd-hzh，模块实验设计，高级内存管理

搭载在milk-256M开发板的内嵌式Linux系统，使用本地ASR-TTS模型及whisper语音大模型，chatgpt-4语言大模型，并采用milk-256m的TPU加速，实现了搭载在milk-256m上的桌面语音机器人，实现了智能化，小型化。

复旦大学，OpenNet-Pilot，操作系统应用开发

高校名称：杭州电子科技大学 队伍ID：35183 队伍名：下一队 选题方向：操作系统原理赛道 项目名称：基于RISC-V的宏内核操作系统MeteorOS

山东大学，T202610422999875，proj45，沙丁鱼的操作系统

相变存储器（PCM）具有存储密度高、静态漏电功耗低以及接近 DRAM 的访问延迟等优势，但在写入过程中，尤其是执行 RESET 操作时，由于热扩散效应，仍然容易产生写扰动，对相邻存储单元造成干扰。现有的写扰动缓解方法大多依赖于纠错机制、编码技术或固定策略，因此难以根据工作负载和数据分布的变化进行动态调整。 针对这一问题，本项目提出了一种基于强化学习的异地更新（Out-of-Place Update）方法 RLWM。RLWM 将写地址选择建模为一个强化学习问题，由智能体根据当前写入模式和失效数据块（stale block）的分布，选择能够最小化写扰动的目标位置。通过轻量级在线训练，RLWM 能够根据工作负载变化不断调整策略，实现长期、持续的写扰动抑制。 实验结果表明，RLWM 平均可降低 46% 的写扰动，相较于SOTA进一步降低了 16%。这说明RLWM 能够在较低额外开销下，有效缓解 PCM 的写扰动问题。

Phase Change Memory (PCM) offers high density, low leakage power, and near-DRAM latency, but remains vulnerable to write disturbance, especially during RESET operations where thermal diffusion may disturb adjacent cells. Existing mitigation methods rely on error correction, coding, or fixed heuristics, and therefore adapt poorly to changing workloads and data patterns. We propose RLWM, a reinforcement-learning-based out-of-place update scheme that mitigates write disturbance in PCM. RLWM formulates write-address selection as a reinforcement learning problem, where the agent selects a target location that minimizes write disturbance based on the current write pattern and stale-block state. Through lightweight training, RLWM adapts online to workload changes and achieves long-term write-disturbance mitigation. Experiments show that RLWM reduces write disturbance by 46% on average and achieves a further 16% reduction compared with the state-of-the-art method. These results demonstrate that RLWM provides effective write-disturbance mitigation for PCM with low additional overhead.

相变存储器（PCM）具有存储密度高、静态漏电功耗低以及接近 DRAM 的访问延迟等优势，但在写入过程中，尤其是执行 RESET 操作时，由于热扩散效应，仍然容易产生写扰动，对相邻存储单元造成干扰。现有的写扰动缓解方法大多依赖于纠错机制、编码技术或固定策略，因此难以根据工作负载和数据分布的变化进行动态调整。 针对这一问题，本项目提出了一种基于强化学习的异地更新（Out-of-Place Update）方法 RLWM。RLWM 将写地址选择建模为一个强化学习问题，由智能体根据当前写入模式和失效数据块（stale block）的分布，选择能够最小化写扰动的目标位置。通过轻量级在线训练，RLWM 能够根据工作负载变化不断调整策略，实现长期、持续的写扰动抑制。 实验结果表明，RLWM 平均可降低 46% 的写扰动，相较于SOTA进一步降低了 16%。这说明RLWM 能够在较低额外开销下，有效缓解 PCM 的写扰动问题。

Phase Change Memory (PCM) offers high density, low leakage power, and near-DRAM latency, but remains vulnerable to write disturbance, especially during RESET operations where thermal diffusion may disturb adjacent cells. Existing mitigation methods rely on error correction, coding, or fixed heuristics, and therefore adapt poorly to changing workloads and data patterns. We propose RLWM, a reinforcement-learning-based out-of-place update scheme that mitigates write disturbance in PCM. RLWM formulates write-address selection as a reinforcement learning problem, where the agent selects a target location that minimizes write disturbance based on the current write pattern and stale-block state. Through lightweight training, RLWM adapts online to workload changes and achieves long-term write-disturbance mitigation. Experiments show that RLWM reduces write disturbance by 46% on average and achieves a further 16% reduction compared with the state-of-the-art method. These results demonstrate that RLWM provides effective write-disturbance mitigation for PCM with low additional overhead.