基于RISC-V架构的强化学习容器化方法研究

RISC-V作为近年来最热门的开源指令集架构,被广泛应用于各个特定领域的微处理器,特别是机器学习领域的模块化定制.但是,现有的RISC-V应用需要将传统软件或模型在RISC-V指令集上重新编译或优化,故如何能快速地在RISC-V体系结构上部署、运行和测试机器学习框架是一个亟待解决的技术问题.使用虚拟化技术可以解决跨平台...

A containerization method for reinforcement learning based on RISC-V architecture

As the hottest open-source instruction set architecture in recent years, RISC-V is widely used in a variety of domain-specific microprocessors, especially for modular customization in the field of machine learning. However, existing RISC-V applications require recompilation or optimization of legacy software or models on the RISC-V instruction set. Therefore, how to rapidly deploy, run, and test machine learning frameworks on RISC-V architectures is a pressing technology challenges. The use of virtualization technology can solve the problem of deploying and running models across platforms. However, traditional virtualization techniques, such as virtual machines, are often not applicable to RISC-V architecture scenarios due to their high performance requirements for native systems, high resource footprint, and slow operational response. Discussion of reinforcement learning virtualization on resource-constrained RISC-V architectures. Firstly, by adopting containerization technology, reducing the cost of virtualization for upper-level software builds, removing redundant middleware, and customizing namespaces to isolate specific processes, we effectively improve the resource utilization for learning tasks and achieve the rapid execution of model training. Secondly, the features of the RISC-V instruction set are used to further optimize the upper neural network model and optimize the reinforcement learning efficiency. Finally, a system prototype of the overall optimization and containerization method is implement- ed and the performance evaluation of the prototype is completed by testing multiple benchmark test sets. Containerization techniques enable the rapid deployment and operation of more complex and deep learning software frameworks at a relatively small additional performance cost, compared to traditional methods of cross-compiling deep neural network models in RISC-V architectures. RISC-V based models have approximate deployment time and reduce substantial performance losses compared to the hypervisor VM method. Preliminary experimental results demonstrate that containerization and the optimization method on it are an effective way to achieve the rapid deployment of software and learning models based on RISC-V architecture.