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A Collaborative Weightless Neural Network

In the last few years, the demand for cloud-based high-performance systems has gone hand in hand with the field of Machine Learning (ML), as most deep learning models are increasingly requiring more powerful machines to efficiently handle soaring amounts of data on a daily basis. Although this seems like a good distributed solution, it actually leads to centralizing training and inference in commercially available data centers. Thus, this work presents a collaborative architecture that uses Weightless Neural Networks (WNNs) to enable concurrent training/inference in modern container-based cloud systems, such as Kubernetes and Docker Swarm. A WNN is a class of Artificial Neural Networks (ANNs) that uses a set of Random Access Memories (RAMs) to map training and classification information. Performance results show that our collaborative architecture can efficiently learn and classify well-know datasets in about 2 times faster than traditional architectures, while also being able to scale to many concurrent clients.