Boolean Variation and Boolean Logic BackPropagation
Abstract
A Boolean deep learning model utilizes Boolean numbers and logic for weights and activations, enabling direct training without gradients via logic synthesis and backpropagation.
The notion of variation is introduced for the Boolean set and based on which Boolean logic backpropagation principle is developed. Using this concept, deep models can be built with weights and activations being Boolean numbers and operated with Boolean logic instead of real arithmetic. In particular, Boolean deep models can be trained directly in the Boolean domain without latent weights. No gradient but logic is synthesized and backpropagated through layers.
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