Deep Differentiable Logic Gate Networks

Felix Petersen, Christian Borgelt, Hilde Kühne, Oliver Deussen

Publikation: Beitrag in Buch/Bericht/Konferenzband/GesetzeskommentarKonferenzbeitragPeer-reviewed

Abstract

Recently, research has increasingly focused on developing efficient neural network architectures. In this work, we explore logic gate networks for machine learning tasks by learning combinations of logic gates. These networks comprise logic gates such as "AND" and "XOR", which allow for very fast execution. The difficulty in learning logic gate networks is that they are conventionally non-differentiable and therefore do not allow training with gradient descent. Thus, to allow for effective training, we propose differentiable logic gate networks, an architecture that combines real-valued logics and a continuously parameterized relaxation of the network. The resulting discretized logic gate networks achieve fast inference speeds, e.g., beyond a million images of MNIST per second on a single CPU core.
OriginalspracheEnglisch
TitelProceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022)
PublikationsstatusVeröffentlicht - 28 Nov. 2022

Systematik der Wissenschaftszweige 2012

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