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Found 2 papers, 2 papers with code
FPGA-QHAR: Throughput-Optimized for Quantized Human Action Recognition on The Edge
Accelerating Human Action Recognition (HAR) efficiently for real-time surveillance and robotic systems on edge chips remains a challenging research field, given its high computational and memory requirements.
Hardware-Efficient Deconvolution-Based GAN for Edge Computing
Generative Adversarial Networks (GAN) are cutting-edge algorithms for generating new data samples based on the learned data distribution.
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