RISC-V Summit

The advancement of large language models (LLMs) has significantly enhanced natural language processing capabilities, enabling complex text understanding and generation tasks. This presentation focuses on optimizing the open-source LLaMA CPP project for the RISC-V P extension. By running the TinyLLaMA 1.1B model on the Andes Voyager development board using a quad-core CPU supporting the RISC-V P extension, performance results show that the model can achieve near real-time response. This work highlights the potential of RISC-V as an efficient platform for deploying advanced AI models in resource-constrained environments, contributing to the growing field of edge computing and embedded AI applications.