arXiv:2605.10989v3 Announce Type: replace Abstract: The training of Binary Neural Networks (BNNs) is fundamentally based on gradient approximation for non-differentiable binarization operations (e.g., sign function). However, prevailing methods including the Straight-Through Estimator (STE) and its improved variants, rely on hand-crafted designs that suffer from gradient mismatch problem and information loss induced by fixed-range gradient clipping. To address this, we propose SURrogate GradiEnt Adaptation (SURGE), a novel learnable gradient compensation framework with theoretical grounding. S
This research addresses a fundamental limitation in Binary Neural Network training, a topic of growing interest for efficient AI deployment. The academic publication signifies a maturation in tackling these core challenges.
Improving the training efficiency and accuracy of BNNs is crucial for deploying AI models on resource-constrained devices, impacting edge AI and broader accessibility of advanced computational power.
SURGE proposes a novel, theoretically-grounded method for gradient adaptation in BNNs, potentially leading to more stable and effective training compared to existing heuristics.
- · AI hardware developers
- · Edge AI companies
- · Researchers in neural networks
- · Resource-constrained computing platforms
- · Developers relying solely on traditional ANN architectures
- · Companies with suboptimal BNN training methods
Improved performance and broader adoption of Binary Neural Networks in various applications.
Reduced computational and energy demands for AI models, potentially accelerating AI deployment in new sectors.
Enhanced competition in the edge AI market as more efficient models become feasible on a wider range of hardware.
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Read at arXiv cs.LG