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Found 7 papers, 3 papers with code
YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection
As such, there has been growing research interest in the design of efficient deep neural network architectures catered for edge and mobile usage.
EdgeSegNet: A Compact Network for Semantic Segmentation
In this study, we introduce EdgeSegNet, a compact deep convolutional neural network for the task of semantic segmentation.
AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design
Furthermore, the efficacy of the AttoNets is demonstrated for the task of instance-level object segmentation and object detection, where an AttoNet-based Mask R-CNN network was constructed with significantly fewer parameters and computational costs (~5x fewer multiply-add operations and ~2x fewer parameters) than a ResNet-50 based Mask R-CNN network.
FermiNets: Learning generative machines to generate efficient neural networks via generative synthesis
In this study, we introduce the idea of generative synthesis, which is premised on the intricate interplay between a generator-inquisitor pair that work in tandem to garner insights and learn to generate highly efficient deep neural networks that best satisfies operational requirements.
Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection
The resulting Tiny SSD possess a model size of 2. 3MB (~26X smaller than Tiny YOLO) while still achieving an mAP of 61. 3% on VOC 2007 (~4. 2% higher than Tiny YOLO).
StressedNets: Efficient Feature Representations via Stress-induced Evolutionary Synthesis of Deep Neural Networks
The computational complexity of leveraging deep neural networks for extracting deep feature representations is a significant barrier to its widespread adoption, particularly for use in embedded devices.
SquishedNets: Squishing SqueezeNet further for edge device scenarios via deep evolutionary synthesis
While deep neural networks have been shown in recent years to outperform other machine learning methods in a wide range of applications, one of the biggest challenges with enabling deep neural networks for widespread deployment on edge devices such as mobile and other consumer devices is high computational and memory requirements.
