The rapid rise of artificial intelligence has led to an unsustainable growth in energy consumption. This has motivated progress in neuromorphic computing and physics-based training of learning machines as alternatives to digital neural networks. Many theoretical studies focus on simple architectures like all-to-all or densely connected layered networks. However, these may be challenging to realize experimentally, e.g. due to connectivity constraints. In this work, we investigate the performance of the widespread physics-based training method of equilibrium propagation for more realistic architectural choices, specifically, locally connected lattices. We train an XY model and explore the influence of architecture on various benchmark tasks, tracking the evolution of spatially distributed responses and couplings during training. Our results show that sparse networks with only local connections can achieve performance comparable to dense networks. Our findings provide guidelines for further scaling up architectures based on equilibrium propagation in realistic settings.

翻译：人工智能的快速发展导致了能源消耗的不可持续增长。这推动了神经形态计算和基于物理的学习机器训练作为数字神经网络替代方案的进展。许多理论研究聚焦于简单的架构，例如全连接或密集连接的分层网络。然而，这些架构在实验上可能难以实现，例如由于连接性限制。在本工作中，我们研究了广泛使用的基于物理的训练方法——平衡传播，在更现实的架构选择（特别是局部连接的晶格）上的性能。我们训练了一个XY模型，并探索了架构对各种基准任务的影响，追踪了训练过程中空间分布响应和耦合的演变。我们的结果表明，仅具有局部连接的稀疏网络可以实现与密集网络相当的性能。我们的发现为在现实环境中进一步扩展基于平衡传播的架构提供了指导。