FFT-Based Deep Learning Deployment in Embedded Systems
This addresses the challenge of intensive computation and storage for DNNs in embedded systems, offering a domain-specific improvement for deployment efficiency.
The paper tackles the problem of deploying deep neural networks (DNNs) on embedded systems by proposing an FFT-based training and inference model that reduces computational and storage complexity, achieving extraordinary processing speed on embedded platforms.
Deep learning has delivered its powerfulness in many application domains, especially in image and speech recognition. As the backbone of deep learning, deep neural networks (DNNs) consist of multiple layers of various types with hundreds to thousands of neurons. Embedded platforms are now becoming essential for deep learning deployment due to their portability, versatility, and energy efficiency. The large model size of DNNs, while providing excellent accuracy, also burdens the embedded platforms with intensive computation and storage. Researchers have investigated on reducing DNN model size with negligible accuracy loss. This work proposes a Fast Fourier Transform (FFT)-based DNN training and inference model suitable for embedded platforms with reduced asymptotic complexity of both computation and storage, making our approach distinguished from existing approaches. We develop the training and inference algorithms based on FFT as the computing kernel and deploy the FFT-based inference model on embedded platforms achieving extraordinary processing speed.