Abstract

This paper explores the groundbreaking possibility of running large language models (LLMs) on RISC-V microcontrollers, challenging the conventional wisdom that such tasks require high-performance computing resources.

Introduction

With the rise of edge computing and the need for efficient, low-power devices, the ability to run complex AI models on microcontrollers has become increasingly important. This research demonstrates a novel approach to achieving this goal.

Methodology

We developed a custom optimization pipeline that reduces the computational and memory requirements of LLMs, making them suitable for RISC-V microcontrollers without significant loss in performance.

Results

Our experiments show that it is indeed possible to run LLMs on RISC-V microcontrollers with acceptable latency and accuracy, opening up new possibilities for AI applications in resource-constrained environments.

Conclusion

This breakthrough has the potential to revolutionize the field of edge AI, enabling a new generation of smart devices that can perform complex language tasks without relying on cloud computing.