On dedicated analog hardware, equilibrium propagation is an energy-efficient alternative to backpropagation. In spite of its theoretical guarantees, its application in the AI domain remains limited to the discriminative setting. Meanwhile, despite its high computational demands, generative AI is on the rise. In this paper, we demonstrate the application of Equilibrium Propagation in training a variational autoencoder (VAE) for generative modeling. Leveraging the symmetric nature of Hopfield networks, we propose using a single model to serve as both the encoder and decoder which could effectively halve the required chip size for VAE implementations, paving the way for more efficient analog hardware configurations.

翻译：在专用模拟硬件上，均衡传播是反向传播的一种能效优化替代方案。尽管其具备理论保障，但在人工智能领域的应用仍局限于判别式场景。与此同时，尽管生成式人工智能的计算需求极高，该领域仍在蓬勃发展。本文证明了均衡传播在训练用于生成建模的变分自编码器（VAE）中的应用。利用Hopfield网络的对称特性，我们提出使用单一模型同时充当编码器和解码器，这可将VAE实现所需的芯片面积有效减半，为更高效的模拟硬件配置铺平道路。