Faster deep neural network image processing by using vectorized posit operations on a RISC-V processor

Marco Cococcioni; Federico Rossi; Emanuele Ruffaldi; Sergio Saponara

doi:10.1117/12.2586565

12 April 2021 Faster deep neural network image processing by using vectorized posit operations on a RISC-V processor

Marco Cococcioni, Federico Rossi, Emanuele Ruffaldi, Sergio Saponara

Proceedings Volume 11736, Real-Time Image Processing and Deep Learning 2021; 1173604 (2021) https://doi.org/10.1117/12.2586565
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

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 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.

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Marco Cococcioni, Federico Rossi, Emanuele Ruffaldi, and Sergio Saponara "Faster deep neural network image processing by using vectorized posit operations on a RISC-V processor", Proc. SPIE 11736, Real-Time Image Processing and Deep Learning 2021, 1173604 (12 April 2021); https://doi.org/10.1117/12.2586565

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