Anisotropic-diffusion is a commonly used signal preprocessing technique that allows extracting meaningful local characteristics from a signal, such as edges in an image and can be used to support higher-level processing tasks, such as shape detection. This paper presents a fully scalable CMOS-RRAM architecture of an edge-aware-anisotropic filtering algorithm aimed at computer vision applications. The CMOS circuitry controls the scale-space image data to perform pseudo-parallel in-memory computing and nonlinear processing through RRAM crossbar. The arithmetic operations for in-memory computation of brightness gradients are efficiently accumulated to produce the enhanced image in several iterations. The proposed architecture uses single RRAM as a computing and storage element to perform both arithmetic operations and accumulations. Thanks to the in-memory computation, memory accesses and arithmetic operations are reduced by 64% and 92%, respectively, compared to traditional digital implementations. This, in turn, results in a potential reduction of power and area costs of about 75% and 85%, respectively. The processing time is also reduced by 67%.

Zayer, F., Mohammad, B., Saleh, H., Gianini, G. (2020). RRAM Crossbar-based In-Memory Computation of Anisotropic Filters for Image Preprocessing. IEEE ACCESS, 8, 127569-127580 [10.1109/ACCESS.2020.3004184].

RRAM Crossbar-based In-Memory Computation of Anisotropic Filters for Image Preprocessing

Gianini, G
2020

Abstract

Anisotropic-diffusion is a commonly used signal preprocessing technique that allows extracting meaningful local characteristics from a signal, such as edges in an image and can be used to support higher-level processing tasks, such as shape detection. This paper presents a fully scalable CMOS-RRAM architecture of an edge-aware-anisotropic filtering algorithm aimed at computer vision applications. The CMOS circuitry controls the scale-space image data to perform pseudo-parallel in-memory computing and nonlinear processing through RRAM crossbar. The arithmetic operations for in-memory computation of brightness gradients are efficiently accumulated to produce the enhanced image in several iterations. The proposed architecture uses single RRAM as a computing and storage element to perform both arithmetic operations and accumulations. Thanks to the in-memory computation, memory accesses and arithmetic operations are reduced by 64% and 92%, respectively, compared to traditional digital implementations. This, in turn, results in a potential reduction of power and area costs of about 75% and 85%, respectively. The processing time is also reduced by 67%.
Articolo in rivista - Articolo scientifico
anisotropic diffusion; image enhancement; in memory computing; RRAM crossbar; Scale-space image;
English
2020
8
127569
127580
9122505
open
Zayer, F., Mohammad, B., Saleh, H., Gianini, G. (2020). RRAM Crossbar-based In-Memory Computation of Anisotropic Filters for Image Preprocessing. IEEE ACCESS, 8, 127569-127580 [10.1109/ACCESS.2020.3004184].
File in questo prodotto:
File Dimensione Formato  
Zayer-2020-IEEE Access-VoR.pdf

accesso aperto

Tipologia di allegato: Publisher’s Version (Version of Record, VoR)
Licenza: Creative Commons
Dimensione 2.59 MB
Formato Adobe PDF
2.59 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/454834
Citazioni
  • Scopus 11
  • ???jsp.display-item.citation.isi??? 9
Social impact