High performance scalable hardware SOM architecture for real-time vector quantization - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2018

High performance scalable hardware SOM architecture for real-time vector quantization

Slavisa Jovanovic
Hassan Rabah
Serge Weber

Résumé

Real-time vector quantization requires high performances and high configurability. Unsupervised neural networks such as Self-organizing maps (SOMs) have been commonly used as vector quantizers in many application domains, mainly in software. The software counterparts of SOMs are highly flexible with limited performances and cannot be used in real-time applications where tight timing constraints require high performances. The flexibility of software SOMs and the performances of their hardware counterparts must be coupled to obtain highly configurable, scalable and application independent SOMs. In this paper, a massively parallel hardware implementation of a scalable and flexible self-organizing map (SOM) is presented. The scalability and flexibility are obtained by using highly connected communication approach tailored to SOM needs, while the distributed clusters of neuro-processors are at the origin of high performances. The proposed architecture is validated on different map sizes (up to 16×16) with different vector widths (up to 256) applied for real-time vector quantization.
Fichier non déposé

Dates et versions

hal-03982206 , version 1 (10-02-2023)

Identifiants

Citer

Slavisa Jovanovic, Hassan Rabah, Serge Weber. High performance scalable hardware SOM architecture for real-time vector quantization. 2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS), Dec 2018, Sophia Antipolis, France. ⟨10.1109/IPAS.2018.8708863⟩. ⟨hal-03982206⟩
9 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More