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Found 9 papers, 2 papers with code
Combining Neural Architecture Search and Automatic Code Optimization: A Survey
Two notable techniques employed to achieve this goal are Hardware-aware Neural Architecture Search (HW-NAS) and Automatic Code Optimization (ACO).
Hardware Aware Neural Architecture Search
Neural Architecture Search
+1
Analog In-Memory Computing with Uncertainty Quantification for Efficient Edge-based Medical Imaging Segmentation
This work investigates the role of the emerging Analog In-memory computing (AIMC) paradigm in enabling Medical AI analysis and improving the certainty of these models at the edge.
FLASH-RL: Federated Learning Addressing System and Static Heterogeneity using Reinforcement Learning
In fall detection using the MobiAct dataset, FLASH-RL outperforms FedAVG by up to 2. 82% in model's performance and reduces latency by up to 34. 75%.
Grassroots Operator Search for Model Edge Adaptation
The mathematical instructions are then used as the basis for searching and selecting efficient replacement operators that maintain the accuracy of the original model while reducing computational complexity.
Hardware Aware Neural Architecture Search
Neural Architecture Search
AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing
Digital processors based on typical von Neumann architectures are not conducive to edge AI given the large amounts of required data movement in and out of memory.
Treasure What You Have: Exploiting Similarity in Deep Neural Networks for Efficient Video Processing
Deep learning has enabled various Internet of Things (IoT) applications.
Skip Connections in Spiking Neural Networks: An Analysis of Their Effect on Network Training
Spiking neural networks (SNNs) have gained attention as a promising alternative to traditional artificial neural networks (ANNs) due to their potential for energy efficiency and their ability to model spiking behavior in biological systems.
HyT-NAS: Hybrid Transformers Neural Architecture Search for Edge Devices
Vision Transformers have enabled recent attention-based Deep Learning (DL) architectures to achieve remarkable results in Computer Vision (CV) tasks.
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Image Classification
on Visual Wake Words
Hardware Aware Neural Architecture Search
Image Classification
+3
A Comprehensive Survey on Hardware-Aware Neural Architecture Search
Arguably their most significant impact has been in image classification and object detection tasks where the state of the art results have been obtained.
Hardware Aware Neural Architecture Search
Image Classification
+4
