TCL delivers 16.8x faster tuning on CPU and 12.48x on GPU with modestly lower inference latency by combining RDU active sampling, a lightweight Mamba cost model, and cross-platform continual knowledge distillation.
InProceedings of the 29th ACM/IEEE International Symposium on Low Power Electronics and Design(2024), pp
2 Pith papers cite this work. Polarity classification is still indexing.
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AILFM uses active imitation learning to learn thermal- and kernel-aware scheduling policies for LFM inference on 3D S-NUCA many-cores, outperforming baselines while maintaining thermal safety.
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TCL: Enabling Fast and Efficient Cross-Hardware Tensor Program Optimization via Continual Learning
TCL delivers 16.8x faster tuning on CPU and 12.48x on GPU with modestly lower inference latency by combining RDU active sampling, a lightweight Mamba cost model, and cross-platform continual knowledge distillation.
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Active Imitation Learning for Thermal- and Kernel-Aware LFM Inference on 3D S-NUCA Many-Cores
AILFM uses active imitation learning to learn thermal- and kernel-aware scheduling policies for LFM inference on 3D S-NUCA many-cores, outperforming baselines while maintaining thermal safety.