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Found 6 papers, 4 papers with code
Vision Transformer Computation and Resilience for Dynamic Inference
To create dynamic models, we leverage the resilience of vision transformers to pruning and switch between different scaled versions of a model.
Dataset Culling: Towards Efficient Training Of Distillation-Based Domain Specific Models
We develop fixed-angle, long-duration video datasets across several domains, and we show that the dataset size can be culled by a factor of 300x to reduce the total training time by 47x with no accuracy loss or even with slight improvement.
Training Domain Specific Models for Energy-Efficient Object Detection
For the limited domain, we observed that compact DSMs significantly surpass the accuracy of COCO trained models of the same size.
DNN Dataflow Choice Is Overrated
Many DNN accelerators have been proposed and built using different microarchitectures and program mappings.
Distributed, Parallel, and Cluster Computing
Programming Heterogeneous Systems from an Image Processing DSL
We address this problem by extending the image processing language, Halide, so users can specify which portions of their applications should become hardware accelerators, and then we provide a compiler that uses this code to automatically create the accelerator along with the "glue" code needed for the user's application to access this hardware.
Software Engineering
A Systematic Approach to Blocking Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are the state of the art solution for many computer vision problems, and many researchers have explored optimized implementations.
