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Nanyang Technological University, Singapore
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FedTR achieves 95.5% accuracy in label defect identification, rivaling centralized models while preserving data privacy in federated learning settings.
Reducing ResNet-50's computational load by nearly 50% while boosting accuracy demonstrates a breakthrough in deploying CNNs on embedded devices.
Achieving over 100脳 reduction in computing power without sacrificing accuracy, this framework revolutionizes DNN inference on crossbar-based accelerators.
Smart Scissor reduces CNN computational costs by over 40% while actually improving accuracy, challenging the notion that efficiency comes at the expense of performance.
Heterogeneous models can be collaboratively trained to improve accuracy under strict latency constraints, achieving significant performance gains without extra training costs.
