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Nanyang Technological University
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Ignoring cache coherence in task mapping can lead to a staggering 47.85% increase in link utilization and energy waste鈥擟oTM flips this narrative.
CoCo achieves an 88.46% reduction in link utilization while effectively managing coherence overhead, revolutionizing many-core system optimization.
TECO achieves a groundbreaking balance between accuracy and efficiency by pruning CNNs across multiple dimensions, setting a new standard for embedded hardware performance.
FedTR achieves 95.5% accuracy in label defect identification, rivaling centralized models while preserving data privacy in federated learning settings.
Achieving over 100脳 reduction in computing power without sacrificing accuracy, this framework revolutionizes DNN inference on crossbar-based accelerators.
