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.

该研究论文介绍了平衡传播在训练变分自编码器中的应用，利用对称的 Hopfield 网络结构，提出使用一种模型兼作编码器和解码器，可有效减少 VAE 实施所需芯片尺寸，为更高效的模拟硬件配置铺平了道路。

使用平衡传播训练 Hopfield 变分自编码器

Training a Hopfield Variational Autoencoder with Equilibrium Propagation

The fast execution speed and energy efficiency of analog hardware has made
them a strong contender for deployment of deep learning model at the edge.
However, there are concerns about the presence of analog noise which causes
changes to the weight of the models, leading to performance degradation of deep
learning model, despite their inherent noise resistant characteristics. The
effect of the popular batch normalization layer on the noise resistant ability
of deep learning model is investigated in this work. This systematic study has
been carried out by first training different models with and without batch
normalization layer on CIFAR10 and CIFAR100 dataset. The weights of the
resulting models are then injected with analog noise and the performance of the
models on the test dataset is obtained and compared. The results show that the
presence of batch normalization layer negatively impacts noise resistant
property of deep learning model and the impact grows with the increase of the
number of batch normalization layers.

通过在 CIFAR10 和 CIFAR100 数据集上测试，研究了批规范化层对深度学习模型抗噪声能力的影响，结果表明批规范化层会对模型的噪声抗性产生负面影响，影响程度随批规范化层数增加而增加。