In recent years, the AIoT(AI-powered Internet-of-Things) market is growing, but the hardware limitations, such as limited computation and limited storage, of Cortex-M devices impede AIoT development for years. In this talk, we will introduce how ONNC accelerates models on PyTorch for TinyML by the ONNC compiler. With the help of the ONNC compilation, we could reduce hardware resource consumptions for models. In a Cortex-M4 device, compared with a MobileNet compiled by TensorFlow for Micro, a MobileNet compiled by ONNC with Visual Wake Words dataset can accelerate 1.27 times, reduce 1.57 times of memory consumption, and reduce 7.12 times of code size.
About Peter Chang
Peter is the co-founder of Skymizer Taiwan Inc. His research interests span areas in operating systems, virtualization, and computer architecture. Currently, he focuses on topics in hardware/software co-design. He was also the maintainer of SkyPat, an open-source performance unit-test suite, and ARMvisor, one of the Kernel-based Virtual Machine solutions on ARM architecture.