Gradient-Free Training of Quantized Neural Networks

• URL: http://arxiv.org/abs/2410.09734v2
• Date: Mon, 29 Sep 2025 08:57:27 GMT
• Title: Gradient-Free Training of Quantized Neural Networks
• Authors: Noa Cohen, Omkar Joglekar, Dotan Di Castro, Vladimir Tchuiev, Shir Kozlovsky, Michal Moshkovitz,
• Abstract summary: Training neural networks requires significant computational resources and energy.<n>Mixed-precision and quantization-aware training reduce bit usage, yet they still depend heavily on computationally expensive gradient-based optimization.<n>We propose a paradigm shift: eliminate gradients altogether.
• Score: 9.348959582516438
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Training neural networks requires significant computational resources and energy. Methods like mixed-precision and quantization-aware training reduce bit usage, yet they still depend heavily on computationally expensive gradient-based optimization. In this work, we propose a paradigm shift: eliminate gradients altogether. One might hope that, in a finite quantized space, finding optimal weights with out gradients would be easier but we theoretically prove that this problem is NP-hard even in simple settings where the continuous case is efficiently solvable. To address this, we introduce a novel heuristic optimization framework that avoids full weight updates and significantly improves efficiency. Empirically, our method achieves performance comparable to that of full-precision gradient-based training on standard datasets and architectures, while using up to 3x less energy and requiring up to 5x fewer parameter updates.

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