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Found 6 papers, 1 papers with code
LeMo-NADe: Multi-Parameter Neural Architecture Discovery with LLMs
Building efficient neural network architectures can be a time-consuming task requiring extensive expert knowledge.
Third-Party Hardware IP Assurance against Trojans through Supervised Learning and Post-processing
We present a comprehensive framework, associated algorithms, and a tool flow for obtaining an optimal set of features, training a targeted machine learning model, detecting suspect nets, and identifying Trojan circuitry from the suspect nets.
Neural Storage: A New Paradigm of Elastic Memory
In NS, the network structure, strength of associations, and granularity of the data adjust continuously during system operation, providing unprecedented plasticity and performance benefits.
Leveraging Domain Knowledge using Machine Learning for Image Compression in Internet-of-Things
For these applications, image compression commonly requires: (1) maintaining features for coarse-grain pattern recognition instead of the high-level details for human perception due to machine-to-machine communications; (2) high compression ratio that leads to improved energy and transmission efficiency; (3) large dynamic range of compression and an easy trade-off between compression factor and quality of reconstruction to accommodate a wide diversity of IoT applications as well as their time-varying energy/performance needs.
SAIL: Machine Learning Guided Structural Analysis Attack on Hardware Obfuscation
Obfuscation is a technique for protecting hardware intellectual property (IP) blocks against reverse engineering, piracy, and malicious modifications.
Cryptography and Security
Learning to Estimate Pose by Watching Videos
In this paper we propose a technique for obtaining coarse pose estimation of humans in an image that does not require any manual supervision.
