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GPTQT: Quantize Large Language Models Twice to Push the Efficiency
Compared to the strong 3-bit quantization baseline, GPTQT further reduces perplexity by 4. 01 on opt-66B and increases speed by 1. 24 times on opt-30b.
ShiftAddAug: Augment Multiplication-Free Tiny Neural Network with Hybrid Computation
Remarkably, it secures up to a 4. 95% increase in accuracy on the CIFAR100 compared to its directly trained counterparts, even surpassing the performance of multiplicative NNs.
Efficient Fusion and Task Guided Embedding for End-to-end Autonomous Driving
To address the challenges of sensor fusion and safety risk prediction, contemporary closed-loop autonomous driving neural networks leveraging imitation learning typically require a substantial volume of parameters and computational resources to run neural networks.
Unleashing the Potential of Spiking Neural Networks for Sequential Modeling with Contextual Embedding
The human brain exhibits remarkable abilities in integrating temporally distant sensory inputs for decision-making.
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