用1比特逻辑运算提升布尔网络性能，降低计算开销。
Towards Narrowing the Generalization Gap in Deep Boolean Networks
- 引入逻辑跳跃连接与保空间采样策略优化网络结构
- 在视觉任务中实现超越现有方法的精度与效率
- 适合关注硬件加速与低功耗AI的开发者参考
深度神经网络规模与复杂度的快速增长显著增加了计算需求，制约其在真实场景中的高效部署。基于逻辑门构建的布尔网络提供了一种硬件友好的替代方案，有望实现更高效的实现。然而，其性能是否可媲美传统网络仍不明确。本文探索提升深度布尔网络的策略，以期超越传统模型。提出逻辑跳跃连接与保空间采样等新方法，并在广泛使用的视觉数据集上验证，结果显著优于现有方法。分析表明，通过1比特逻辑运算，深度布尔网络可在保持高性能的同时大幅降低计算成本。这些发现表明，布尔网络是实现高效、高性能深度学习模型的有前景方向，对推动硬件加速人工智能应用具有重要意义。
原文摘要 · Abstract (English)
The rapid growth of the size and complexity in deep neural networks has sharply increased computational demands, challenging their efficient deployment in real-world scenarios. Boolean networks, constructed with logic gates, offer a hardware-friendly alternative that could enable more efficient implementation. However, their ability to match the performance of traditional networks has remained uncertain. This paper explores strategies to enhance deep Boolean networks with the aim of surpassing their traditional counterparts. We propose novel methods, including logical skip connections and spatiality preserving sampling, and validate them on vision tasks using widely adopted datasets, demonstrating significant improvement over existing approaches. Our analysis shows how deep Boolean networks can maintain high performance while minimizing computational costs through 1-bit logic operations. These findings suggest that Boolean networks are a promising direction for efficient, high-performance deep learning models, with significant potential for advancing hardware-accelerated AI applications.
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