From MNIST to ImageNet: Understanding the Scalability Boundaries of Differentiable Logic Gate Networks

Differentiable Logic Gate Networks (DLGNs) are a very fast and energy-efficient alternative to conventional feed-forward networks. With learnable combinations of logical gates, DLGNs enable fast inference by hardware-friendly execution. Since the concept of DLGNs has only recently gained attention, these networks are still in their developmental infancy, including the design and scalability of their output layer. To date, this architecture has primarily been tested on datasets with up to ten classes. This work examines the behavior of DLGNs on large multi-class datasets. We investigate its general expressiveness, its scalability, and evaluate alternative output strategies. Using both synthetic and real-world datasets, we provide key insights into the importance of temperature tuning and its impact on output layer performance. We evaluate conditions under which the Group-Sum layer performs well and how it can be applied to large-scale classification of up to 2000 classes. Figure 1: DLGNs (blue) consistently outperform MLPs (red) across classification tasks with up to 2000 classes. The result illustrates the potential of logic-gate-based architectures to remain effective when applied to large-scale classification problems. Deep artificial neural networks have improved immensely in the last few years, exhibiting impressive performance across a wide range of tasks (Golroudbari & Sabour, 2023; Noor & Ige, 2024; Ekun-dayo & Ezugwu, 2025). However, these improvements come with rapidly growing computational costs (Thompson et al., 2020; Rosenfeld, 2021; Tripp et al., 2024). This constrains their deployment in many real-world environments, particularly on edge devices and mobile phones (Zhang et al., 2020; Zheng, 2025).