Beyond Discreteness: Sample Complexity Analysis of Straight-Through Estimator for 1-bit Quantization
arXiv:2505.18113v2 Announce Type: replace Abstract: Training quantized neural networks requires addressing the non-differentiable and discrete nature of the underlying optimization problem. To tackle this challenge, the straight-through estimator (STE) has become the most widely adopted heuristic, allowing backpropagation through discrete operations by introducing biased yet valid surrogate gradients. However, its theoretical properties remain largely unexplored, with few existing analyses focus on the generalization error by assuming an infinite amount of training data. In contrast, this work
The continuous drive for more efficient AI hardware and software is pushing research into fundamental problems like quantization, making theoretical advancements on STE timely.
Improved theoretical understanding of straight-through estimators can lead to more robust and efficient quantized neural networks, crucial for deploying AI on constrained hardware.
A deeper theoretical foundation for quantization techniques helps advance the practical implementation of AI models, particularly for energy-efficient edge devices.
- · AI hardware manufacturers
- · Edge AI developers
- · AI research institutions
- · Less optimized AI architectures
More energy-efficient and smaller AI models become deployable on diverse hardware platforms.
This could accelerate the proliferation of AI into new embedded systems and pervasive computing applications.
The reduced computational overhead might lower the power requirements for large-scale AI infrastructure, impacting the broader energy landscape for AI.
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