arXiv:2512.04189v1 Announce Type: new 
Abstract: Binary Neural Networks (BNNs), which constrain both weights and activations to binary values, offer substantial reductions in computational complexity, memory footprint, and energy consumption. These advantages make them particularly well suited for deployment on resource-constrained devices. However, training BNNs via gradient-based optimization remains challenging due to the discrete nature of their variables. The dominant approach, quantization-aware training, circumvents this issue by employing surrogate gradients. Yet, this method requires maintaining latent full-precision parameters and performing the backward pass with floating-point arithmetic, thereby forfeiting the efficiency of binary operations during training. While alternative approaches based on local learning rules exist, they are unsuitable for global credit assignment and for back-propagating errors in multi-layer architectures. This paper introduces Binary Error Propagation (BEP), the first learning algorithm to establish a principled, discrete analog of the backpropagation chain rule. This mechanism enables error signals, represented as binary vectors, to be propagated backward through multiple layers of a neural network. BEP operates entirely on binary variables, with all forward and backward computations performed using only bitwise operations. Crucially, this makes BEP the first solution to enable end-to-end binary training for recurrent neural network architectures. We validate the effectiveness of BEP on both multi-layer perceptrons and recurrent neural networks, demonstrating gains of up to +6.89% and +10.57% in test accuracy, respectively. The proposed algorithm is released as an open-source repository.

يقدم مقال جديد خوارزمية انتشار الخطأ الثنائي (BEP) المصممة لتحسين تدريب الشبكات العصبية الثنائية (BNNs)، التي تتميز بأوزانها وتفعيلات ثنائية. تتناول هذه الخوارزمية التحديات المتعلقة بالتحسين القائم على التدرجات في الشبكات العصبية الثنائية، التي تعتمد تقليديًا على طرق التدريب الواعية بالتكميم والتي تؤثر على الكفاءة خلال عملية التدريب.

Un nuevo artículo presenta el algoritmo de Propagación de Errores Binarios (BEP) diseñado para mejorar el entrenamiento de las Redes Neuronales Binarias (BNNs), que se caracterizan por sus pesos y activaciones binarias. Este algoritmo aborda los desafíos de la optimización basada en gradientes en las BNNs, que tradicionalmente dependen de métodos de entrenamiento conscientes de la cuantización que comprometen la eficiencia durante el proceso de entrenamiento.

Un nouvel article présente l'algorithme de propagation d'erreur binaire (BEP) conçu pour améliorer l'entraînement des réseaux neuronaux binaires (BNNs), caractérisés par leurs poids et activations binaires. Cet algorithme aborde les défis de l'optimisation basée sur le gradient dans les BNNs, qui s'appuient traditionnellement sur des méthodes d'entraînement conscientes de la quantification qui compromettent l'efficacité pendant le processus d'entraînement.

A new paper presents the Binary Error Propagation (BEP) algorithm designed to enhance the training of Binary Neural Networks (BNNs), which are characterized by their binary weights and activations. This algorithm addresses the challenges of gradient-based optimization in BNNs, which traditionally rely on quantization-aware training methods that compromise efficiency during the training process.

BEP: A Binary Error Propagation Algorithm for Binary Neural Networks Training

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