Fat-Fast VG-RAM WNN: A high performance approach

The Virtual Generalizing Random Access Memory Weightless Neural Network (VGRAM WNN) is a type of WNN that only requires storage capacity proportional to the training set. As such, it is an effective machine learning technique that offers simple implementation and fast training – it can be made in one shot. However, the VG-RAM WNN test time for applications that require many training samples can be large, since it increases with the size of the memory of each neuron. In this paper, we present Fat-Fast VG-RAM WNNs. Fat-Fast VG-RAM WNNs employ multi-index chained hashing for fast neuron memory search. Our chained hashing technique increases the VG-RAM memory consumption (fat) but reduces test time substantially (fast), while keeping most of its machine learning performance. To address the memory consumption problem, we employ a data clustering technique to reduce the overall size of the neurons’ memory. This can be achieved by replacing clusters of neurons’ memory by their respecti...

• Type: Journal
• Year: 2016
• Where: IJON
• Authors: Avelino Forechi, Alberto F. De Souza, Jorcy de Oliveira Neto, Edilson de Aguiar, Claudine Badue, Artur S. d'Avila Garcez, Thiago Oliveira-Santos
• Keywords: IJON 2016