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.

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

Cococcioni M.
Co-primo
;
Rossi F.
Co-primo
;
Ruffaldi E.
Co-primo
;
Saponara S.
Co-primo
2021-01-01

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.
2021
9781510643093
9781510643109
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1116902
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