Search Results for author:
Found 8 papers, 3 papers with code
Model for Peanuts: Hijacking ML Models without Training Access is Possible
We also provide insights on over-parameterization as one possible inherent factor that makes model hijacking easier, and we accordingly propose a compression-based countermeasure against this attack.
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
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
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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.
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.
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.
