In 2017, the Google research team proposed the Transformer architecture in their paper "Attention Is All You Need", ushering in a new era of deep learning. In the past decade, Transformer has become the cornerstone architecture for natural language processing, computer vision, and multimodal learning. However, as the scale of the model continues to expand and the inference cost continues to rise, researchers are beginning to explore more efficient alternative solutions.
1、 The success and limitations of Transformer
The core innovation of Transformer lies in its self attention mechanism, which allows the model to directly capture the dependency relationship between any two positions when processing sequential data. This mechanism endows Transformer with powerful expressive power, but at the same time, it also brings a computational complexity of O (L ²) (where L is the sequence length). When processing long sequences, such as whole books, long videos, or high-resolution images, the computational load and video memory consumption increase exponentially, becoming the main bottleneck in practical applications.
2、 The Rise of State Space Models (SSM)
Between 2024 and 2026, State Space Models (SSM) will become the most popular direction in the field of AI architecture. The SSM architecture represented by Mamba abandons self attention mechanism and instead adopts state space representation inspired by control theory. Mamba achieves a linear complexity of O (L) through the Selective State Space mechanism. When processing long sequences of over 100000 tokens, it is 5-10 times faster than Transformers of the same scale, and its video memory usage is only one-third of the latter.
3、 Hybrid architecture: complementing each other's strengths and weaknesses
The latest trend is not an either or choice, but the rise of hybrid architectures. Models such as Jamba and Samba alternate the stacking of Transformer's attention layer and SSM layer, retaining the long-range dependency capture capability of the attention mechanism in key positions and using SSM to achieve efficient processing in other positions. This design achieves comparable or even better performance than pure Transformers on long context tasks, while significantly reducing computational costs.
4、 Hardware friendly design
Another important feature of the new generation architecture is hardware aware design. The Scan Operation of SSM is highly adapted to the parallel computing characteristics of GPUs, making full use of tensor cores and video memory bandwidth. In addition, the maturity of quantitative perception training and sparse activation techniques enables these new architectures to achieve efficient inference on consumer grade GPUs.
5、 Future prospects
The evolution of AI architecture is far from over. The directions of Neuro Symbolic, Liquid Neural Networks, and Physics Inspired Architects are emerging. It can be foreseen that future AI models will no longer rely on a single architecture, but dynamically select the optimal computing path based on different task requirements - this will be a true "intelligent" architecture.