提出可调精度的二值神经网络构造方法，理论保证高准确率。
G-Net: A Provably Easy Construction of High-Accuracy Random Binary Neural Networks
- 基于高维计算思想，用汉明距离构建二值嵌入
- 在CIFAR-10上比前代高维模型高出近30%准确率
- 理论可证且适合硬件部署，为二值化模型提供新思路
我们提出一种新型随机算法，用于构建可调精度的二值神经网络。该方法受高维计算（HDC）启发，利用高维向量表示，具备高效硬件实现和抗模型退化能力。与传统低精度量化不同，我们以超立方体中的点形式对数据进行二值嵌入，并采用汉明距离度量。我们提出一类新的浮点神经网络——G-Nets，其通用性足以模拟标准网络层。每个浮点G-Net包含一个随机二值嵌入的嵌入式高维（EHD）G-Net，因测度集中性而理论上保持浮点模型精度。实验表明，我们的二值模型在分类准确率上达到卷积神经网络水平，且在CIFAR-10上相比先前的HDC模型提升近30%。G-Nets为神经网络与随机二值网络之间建立了理论可靠桥梁，开辟了构建鲁棒二值/量化深度学习模型的新方向。代码已开源：https://github.com/GNet2025/GNet。
原文摘要 · Abstract (English)
We propose a novel randomized algorithm for constructing binary neural networks with tunable accuracy. This approach is motivated by hyperdimensional computing (HDC), which is a brain-inspired paradigm that leverages high-dimensional vector representations, offering efficient hardware implementation and robustness to model corruptions. Unlike traditional low-precision methods that use quantization, we consider binary embeddings of data as points in the hypercube equipped with the Hamming distance. We propose a novel family of floating-point neural networks, G-Nets, which are general enough to mimic standard network layers. Each floating-point G-Net has a randomized binary embedding, an embedded hyperdimensional (EHD) G-Net, that retains the accuracy of its floating-point counterparts, with theoretical guarantees, due to the concentration of measure. Empirically, our binary models match convolutional neural network accuracies and outperform prior HDC models by large margins, for example, we achieve almost 30\% higher accuracy on CIFAR-10 compared to prior HDC models. G-Nets are a theoretically justified bridge between neural networks and randomized binary neural networks, opening a new direction for constructing robust binary/quantized deep learning models. Our implementation is available at https://github.com/GNet2025/GNet.
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