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Found 4 papers, 3 papers with code
Accelerating Deep Neural Networks via Semi-Structured Activation Sparsity
The demand for efficient processing of deep neural networks (DNNs) on embedded devices is a significant challenge limiting their deployment.
YOLOBench: Benchmarking Efficient Object Detectors on Embedded Systems
We present YOLOBench, a benchmark comprised of 550+ YOLO-based object detection models on 4 different datasets and 4 different embedded hardware platforms (x86 CPU, ARM CPU, Nvidia GPU, NPU).
Dynamic ConvNets on Tiny Devices via Nested Sparsity
This work introduces a new training and compression pipeline to build Nested Sparse ConvNets, a class of dynamic Convolutional Neural Networks (ConvNets) suited for inference tasks deployed on resource-constrained devices at the edge of the Internet-of-Things.
EAST: Encoding-Aware Sparse Training for Deep Memory Compression of ConvNets
The implementation of Deep Convolutional Neural Networks (ConvNets) on tiny end-nodes with limited non-volatile memory space calls for smart compression strategies capable of shrinking the footprint yet preserving predictive accuracy.
