Weightless neural networks as memory segmented bloom filters

Abstract

Weightless Neural Networks (WNNs) are Artificial Neural Networks based on RAM memory broadly explored as solution for pattern recognition applications. Memory-oriented solutions for pattern recognition are typically very simple, and can be easily implemented in hardware and software. Nonetheless, the straightforward implementation of a WNN requires a large amount of memory resources making its adoption impracticable on memory constrained systems. In this paper, we establish a foundational relationship between WNN and Bloom filters, presenting a novel unified framework which encompasses the two. In particular, we indicate that a WNN can be framed as a memory segmented Bloom filter. Leveraging such finding, we propose a new model of WNNs which utilizes Bloom filters to implement RAM nodes. Bloom filters reduce memory requirements, and allow false positives when determining if a given pattern was already seen in data. We experimentally found that for pattern recognition purposes such false positives can build robustness into the system. The experimental results show that our model using Bloom filters achieves competitive accuracy, training time and testing time, consuming up to 6 orders of magnitude less memory resources when compared against the standard Weightless Neural Network model.