TinyGen: Portable and Compact Code Generation for Tiny Machine Learning
Abstract
Tiny machine learning (TinyML) enables low-power microcontrollers to leverage the power of artificial intelligence without relying on remote computing resources. Typically, developing a TinyML application relies primarily on existing TinyML frameworks, which provide runtime APIs for loading and executing machine learning models. However, such framework-based TinyML development has limitations in terms of portability, programmability, and resource efficiency, motivating the need for a new approach to the TinyML development and deployment process.
Fields of Study & Subjects
Machine Learning and Data Classification
Parallel Computing and Optimization Techniques
IoT and Edge/Fog Computing
Publication details
- Journal / source
- Proceedings of the 35th ACM SIGPLAN International Conference on Compiler Construction
- Publisher
- ACM
- Pages
- 131–142
- Publication date
- 28 January 2026