Energy-Efficient Deep Learning Without Backpropagation: A Rigorous Evaluation of Forward-Only Algorithms

• URL: http://arxiv.org/abs/2511.01061v1
• Date: Sun, 02 Nov 2025 19:48:44 GMT
• Title: Energy-Efficient Deep Learning Without Backpropagation: A Rigorous Evaluation of Forward-Only Algorithms
• Authors: Przemysław Spyra, Witold Dzwinel,
• Abstract summary: We present evidence that the Mono-Forward algorithm consistently surpasses optimally tuned BP baseline in classification accuracy.<n>This superior generalization is achieved with profound efficiency gains, including up to 41% less energy consumption and up to 34% faster training.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The long-held assumption that backpropagation (BP) is essential for state-of-the-art performance is challenged by this work. We present rigorous, hardware-validated evidence that the Mono-Forward (MF) algorithm, a backpropagation-free method, consistently surpasses an optimally tuned BP baseline in classification accuracy on its native Multi-Layer Perceptron (MLP) architectures. This superior generalization is achieved with profound efficiency gains, including up to 41% less energy consumption and up to 34% faster training. Our analysis, which charts an evolutionary path from Geoffrey Hinton's Forward-Forward (FF) to the Cascaded Forward (CaFo) and finally to MF, is grounded in a fair comparative framework using identical architectures and universal hyperparameter optimization. We further provide a critical re-evaluation of memory efficiency in BP-free methods, empirically demonstrating that practical overhead can offset theoretical gains. Ultimately, this work establishes MF as a practical, high-performance, and sustainable alternative to BP for MLPs.

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