TinyFormer paper is accepted by IEEE TCAS-I 2025!

👏 Paper title: TinyFormer: Efficient Sparse Transformer Design and Deployment on Tiny Devices. We propose TinyFormer, a framework for developing and deploying resource-efficient transformer models on Microcontrollers (MCUs). Integrating architecture search, sparse model optimization, and automated deployment, it achieves 96.1% accuracy on CIFAR-10 under strict hardware constraints, delivering up to 12.2× inference speedup compared to CMSIS-NN. [related project]