Fast deep neural networks for image processing using posits and ARM scalable vector extension

• DOI: 10.1007/s11554-020-00984-x
• Publication Date: 2020-05-18T19:03:25Z
• Authors: Marco Cococcioni; Federico Rossi; Emanuele Ruffaldi; Sergio Saponara
• Keywords: 01 natural sciences; Auto-vectorization; Autonomous driving; Deep neural networks (DNNs); Posit arithmetic; Real-time image processing; Scalable vector extension; 0104 chemical sciences

With the advent of image processing and computer vision for automotive under real-time constraints, the need for fast and architecture-optimized arithmetic operations is crucial. Alternative and efficient representations for real numbers are starting to be explored, and among them, the recently introduced posit$$^{\mathrm{TM}}$$ number system is highly promising. Furthermore, with the implementation of the architecture-specific mathematical library thoroughly targeting single-instruction multiple-data (SIMD) engines, the acceleration provided to deep neural networks framework is increasing. In this paper, we present the implementation of some core image processing operations exploiting the posit arithmetic and the ARM scalable vector extension SIMD engine. Moreover, we present applications of real-time image processing to the autonomous driving scenario, presenting benchmarks on the tinyDNN deep neural network (DNN) framework.