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Energy-Efficient Memristor Accelerator for Depression Detection with a CNN-LSTM Hybrid Model
Early screening of depression requires efficient analysis of audio and speech data, while conventional CPU and GPU platforms incur high latency and energy consumption for CNN-LSTM inference. To address this challenge, we propose a unified resistive RAM (ReRAM) processing-in-memory accelerator for a
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