Foto 7

M. Cococcioni, F. Rossi, E. Ruffaldi, and S. Saponara. "Faster deep neural network image processing by using vectorized posit operations on a RISC-V processor", SPIE Defense and Commercial Sensing 2021

Written by

Abstract:  Real-time processing of images and videos is becoming considerably crucial in modern applications of machine learning (ML) and deep neural networks. Having a faster and compressed floating point arithmetic can significantly increase the performance of such applications optimizing memory occupation and transfer of information. In this field, the novel \emph{posit} number system is very promising. In this paper we exploit posit numbers to evaluate the performance of several machine learning algorithms in real-time image and video processing applications. Future steps will involve further hardware accelerations for native posit operations.