The Transformer architecture has dominated machine learning in a wide range
of tasks. The specific characteristic of this architecture is an expensive
scaled dot-product attention mechanism that models the inter-token
interactions, which is known to be the reason behind its success. However, such
a mechanism does not have a direct parallel to the human brain which brings the
question if the scaled-dot product is necessary for intelligence with strong
expressive power. Inspired by the lateralization of the human brain, we propose
a new simple but effective architecture called the Lateralization MLP (L-MLP).
Stacking L-MLP blocks can generate complex architectures. Each L-MLP block is
based on a multi-layer perceptron (MLP) that permutes data dimensions,
processes each dimension in parallel, merges them, and finally passes through a
joint MLP. We discover that this specific design outperforms other MLP variants
and performs comparably to a transformer-based architecture in the challenging
diffusion task while being highly efficient. We conduct experiments using
text-to-image generation tasks to demonstrate the effectiveness and efficiency
of L-MLP. Further, we look into the model behavior and discover a connection to
the function of the human brain. Our code is publicly available:
https://github.com/zizhao-hu/L-MLP

基于大脑侧化的灵感，我们提出了一种简单而有效的架构 L-MLP，它基于多层感知器并在处理数据维度时进行排列、并行处理和合并，通过连接的 MLP 传递。我们发现这种设计优于其他 MLP 变体，在挑战性的扩散任务中与基于 Transformer 的架构相媲美，同时具有高效性。使用文本到图像生成任务进行实验，证明了 L-MLP 的有效性和高效性。