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Found 5 papers, 1 papers with code
Characterizing and Taming Resolution in Convolutional Neural Networks
Image resolution has a significant effect on the accuracy and computational, storage, and bandwidth costs of computer vision model inference.
Do CNNs Encode Data Augmentations?
To answer this question, we introduce a systematic approach to investigate which layers of neural networks are the most predictive of augmentation transformations.
A Hardware-Software Blueprint for Flexible Deep Learning Specialization
Specialized Deep Learning (DL) acceleration stacks, designed for a specific set of frameworks, model architectures, operators, and data types, offer the allure of high performance while sacrificing flexibility.
Learning to Optimize Tensor Programs
Efficient implementations of tensor operators, such as matrix multiplication and high dimensional convolution, are key enablers of effective deep learning systems.
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs.
