SEED applies sparse recurrent learning to convolutional recurrent units, cutting synaptic operations by 2 to 6 times while keeping mAP on event-based object detection.
Seneca: building a fully digital neuromorphic processor, design trade-offs and challenges,
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Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection
SEED applies sparse recurrent learning to convolutional recurrent units, cutting synaptic operations by 2 to 6 times while keeping mAP on event-based object detection.