Hardware-aware pruning plus quantization of EFGCN models cuts BRAM use 26–31% across three event datasets with 1.65–5.18% accuracy loss, validated by a ZCU104 proof-of-concept.
Learning to detect objects with a 1 megapixel event camera,
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Hardware-aware Graph Neural Networks prunning for embedded event-based vision
Hardware-aware pruning plus quantization of EFGCN models cuts BRAM use 26–31% across three event datasets with 1.65–5.18% accuracy loss, validated by a ZCU104 proof-of-concept.