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.

Link: https://doi.org/10.1117/12.2586565