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Found 12 papers, 3 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
SONATA: Self-adaptive Evolutionary Framework for Hardware-aware Neural Architecture Search
Our SONATA has seen up to sim$93. 6% Pareto dominance over the native NSGA-II, further stipulating the importance of self-adaptive evolution operators in HW-aware NAS.
Evolutionary Algorithms
Hardware Aware Neural Architecture Search
+1
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
Harmonic-NAS: Hardware-Aware Multimodal Neural Architecture Search on Resource-constrained Devices
In this paper, we propose Harmonic-NAS, a framework for the joint optimization of unimodal backbones and multimodal fusion networks with hardware awareness on resource-constrained devices.
General Classification
Multimodal Text and Image Classification
+1
MaGNAS: A Mapping-Aware Graph Neural Architecture Search Framework for Heterogeneous MPSoC Deployment
To achieve this, MaGNAS employs a two-tier evolutionary search to identify optimal GNNs and mapping pairings that yield the best performance trade-offs.
Treasure What You Have: Exploiting Similarity in Deep Neural Networks for Efficient Video Processing
Deep learning has enabled various Internet of Things (IoT) applications.
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.
Ranked #1 on
Image Classification
on Visual Wake Words
Hardware Aware Neural Architecture Search
Image Classification
+3
Map-and-Conquer: Energy-Efficient Mapping of Dynamic Neural Nets onto Heterogeneous MPSoCs
Heterogeneous MPSoCs comprise diverse processing units of varying compute capabilities.
HADAS: Hardware-Aware Dynamic Neural Architecture Search for Edge Performance Scaling
Dynamic neural networks (DyNNs) have become viable techniques to enable intelligence on resource-constrained edge devices while maintaining computational efficiency.
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
Performance Prediction for Convolutional Neural Networks in Edge Devices
In this paper, we present and compare five (5) of the widely used Machine Learning based methods for execution time prediction of CNNs on two (2) edge GPU platforms.
