EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space Duality

For the deployment of neural networks in resource-constrained environments, prior works have built lightweight architectures with convolution and attention for capturing local and global dependencies, respectively.Recently, the state space model has emerged as an effective global token interaction with its favorable linear computational cost in the number of tokens.Yet, efficient vision backbones built with SSM have been explored less.In this paper, we introduce Efficient Vision Mamba ($\textbf{EfficientViM}$), a novel architecture built on hidden state mixer-based state space duality ($\textbf{HSM-SSD}$) that efficiently captures global dependencies with further reduced computational cost.In the HSM-SSD layer, we redesign the previous SSD layer to enable the channel mixing operation within hidden states.Additionally, we propose multi-stage hidden state fusion to further reinforce the representation power of hidden states, and provide the design alleviating the bottleneck caused by the memory-bound operations.As a result, the EfficientViM family achieves a new state-of-the-art speed-accuracy trade-off on ImageNet-1k, offering up to a 0.7% performance improvement over the second-best model SHViT with faster speed.Further, we observe significant improvements in throughput and accuracy compared to prior works, when scaling images or employing distillation training.