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Found 4 papers, 1 papers with code
Efficient ML Models for Practical Secure Inference
For instance, the model provider could be a diagnostics company that has trained a state-of-the-art DenseNet-121 model for interpreting a chest X-ray and the user could be a patient at a hospital.
SuperShaper: Task-Agnostic Super Pre-training of BERT Models with Variable Hidden Dimensions
Such models need to be deployed on devices across the cloud and the edge with varying resource and accuracy constraints.
Design and Scaffolded Training of an Efficient DNN Operator for Computer Vision on the Edge
The parameter efficiency of FuSeConv and its significant out-performance over depthwise separable convolutions on systolic arrays illustrates their promise as a strong solution on the edge.
FuSeConv: Fully Separable Convolutions for Fast Inference on Systolic Arrays
The resultant computation is systolic and efficiently utilizes the systolic array with a slightly modified dataflow.
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