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Found 4 papers, 2 papers with code
Seesaw: High-throughput LLM Inference via Model Re-sharding
To improve the efficiency of distributed large language model (LLM) inference, various parallelization strategies, such as tensor and pipeline parallelism, have been proposed.
Tempo: Accelerating Transformer-Based Model Training through Memory Footprint Reduction
We implement Tempo and evaluate the throughput, memory usage, and accuracy/loss on the BERT Large pre-training task.
Efficient Adaptive Activation Rounding for Post-Training Quantization
To deal with the runtime overhead, we use a coarse-grained version of the border function.
RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Images
In the first stage, an encoder-decoder like convolutional neural network Angle-View Net(AVN) is proposed to predict the SO(3) orientation of the gripper at every location of the image.
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