AI RESEARCH
Cross-Layer Co-Optimized LSTM Accelerator for Real-Time Gait Analysis
arXiv CS.LG
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ArXi:2604.13543v1 Announce Type: cross Long Short-Term Memory (LSTM) neural networks have penetrated healthcare applications where real-time requirements and edge computing capabilities are essential. Gait analysis that detects abnormal steps to prevent patients from falling is a prominent problem for such applications. Given the extremely stringent design requirements in performance, power dissipation, and area, an Application-Specific Integrated Circuit (ASIC) enables an efficient real-time exploitation of LSTMs for gait analysis, achieving high accuracy.