MedMambaLite-v2: Shared Selective Scan for Efficient Edge Medical Mamba

AI-powered medical imaging devices are increasingly used in clinical workflows to support real-time, accurate diagnosis and decision-making. Recent advances in State-Space Models (SSMs) such as Mamba have shown remarkable performance in capturing long-range dependencies for medical image classification. However, their computational complexity and sequential data flow make them difficult to deploy on hardware, limiting real-time and energy-efficient applications at the edge. To address this challenge, we propose MedMambaLite-v2, a shared selective scan framework enabling effective acceleration on embedded edge platforms. For this aim, we build upon our earlier MedMambaLite, and further extend it in MedMambaLite-v2 through a channel-only transition mechanism that achieves a 1.7$\boldsymbol{\times}$ reduction in operations. We then optimize the Convolution (Conv) branch, and apply knowledge distillation to retain accuracy in a compressed student model. The resulting model is 23$\boldsymbol{\times}$ smaller compared to the MedMamba baseline, with only 1.1% reduction in the overall accuracy evaluated across 10 distinct MedMNIST datasets spanning several imaging modalities. The proposed LiteSS2D hardware design also leverages parallelism across scan directions to enable simultaneous state updates, thereby improving memory efficiency, and further incorporates 8-bit quantization to reduce computational overhead. The reconfigurable FPGA hardware prototype demonstrates 9$\boldsymbol{\times}$ reduction in latency for a parallel implementation compared with a serial baseline. Moreover, MedMambaLite-v2 is implemented and demonstrated through end-to-end inference on MedMNIST images on CPU and GPU platforms. Performance analysis of the proposed approach on NVIDIA Jetson Orin Nano and Raspberry Pi 5 shows up to 63% and 78% reductions in energy per inference, respectively, compared to the baseline.