Hard to Crack Hardware

Fascinating development using Logic locks and Spin alignment. 

Hard-to-Crack Hardware

King Abdullah University of Science and Technology (Saudi Arabia)

November 14, 2022

A team of researchers at Saudi Arabia's King Abdullah University of Science and Technology (KAUST) has created an integrated circuit logic lock that could advance cyberattack-resistant electronic devices. The researchers based the logic lock on a magnetic tunnel junction (MTJ), which uses spintronics to function.

 The MTJ's electronic output relies on the spin alignment of the electrons inside it, and only generates the correct output for the circuit when it receives the appropriate key signal input. KAUST's Yehia Massoud said, "With the advancement in fabrication methods, the possibility of using emerging spintronic device structures in the chip design has increased. These properties make spintronic devices a potential choice for exploring hardware security."

Training for Brain on a Chip Using SNN

First I had heard of this detailed. Probably worth a look.

Brain-on-a-Chip Would Need Little Training  in CNN

KAUST Discovery (Saudi Arabia), April 20, 2021

Researchers at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia used a spiking neural network (SNN) on a microchip as a foundation for developing more efficient hardware-based artificial intelligence systems. KAUST's Wenzhe Guo said SNNs mimic the biological nervous system and can process information faster and more efficiently than artificial neural networks. The researchers created a brain-on-a-chip using a standard FPGA microchip and a spike-timing-dependent plasticity model, which allowed the neuromorphic computing system to learn real-world data patterns without training. Compared to other neural network platforms, the brain-on-a-chip was more than 20 times faster and 200 times more energy efficient. Guo said, "Our ultimate goal is to build a compact, fast and low-energy brain-like hardware computing system.".... ' 

Underwater WiFi

Was unaware of this possibility, of course devices will have to be redesigned to make it readily available.  Only works for a 'few' meters, so the capability narrow and limited.

Angling for Underwater Wi-Fi
KAUST Discovery
June 10, 2020

Researchers at King Abdullah University of Science and Technology in Saudi Arabia built an underwater Wi-Fi system that could enable divers to transmit footage from underwater to the surface instantly by sending data through light beams. Researchers tested the Aqua-Fi system by simultaneously uploading and downloading multimedia between two computers located a few meters apart in static water, recording a maximum data transfer speed of 2.11 megabytes per second. The goal is to use radio waves to send data from a diver's smartphone to a "gateway" device attached to their gear, which would then send the data via a light beam to a computer at the surface connected to the Internet via satellite. Said King Abdullah University’s Basem Shihada, “This is the first time anyone has used the Internet underwater completely wirelessly.  .... " 

Classification of Relational Data

A simplification by classification.   Would this work in any context?   Application to social networks is of interest.

Training Agents to Walk with Purpose   By KAUST Discovery

The new classification algorithm that can dramatically simplify relational data.
Researchers at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia and NortoLifeLock Research Group in France have developed a new classification algorithm for relational data.

Researchers at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia and NortoLifeLock Research Group in France have developed a classification algorithm for relational data that is more accurate and orders of magnitude more efficient than previous methods.

The new algorithm represents a more robust approach to classifying relational data by introducing machine learning techniques.

Classifying relational data involves a search agent taking an exploratory "walk" following the connections among nodes.

The algorithm is a graph-based classification model that trains the agent using a reinforcement learning method, which achieves a better classification result.

The new method "is also generally applicable to any kind of graph-structured data, such as social-network recommendation systems and classification of biomolecules, as well as cybersecurity," says NortonLifeLock researcher Han Yufei.

From KAUST Discovery
View Full Article

Paper:

https://discovery.kaust.edu.sa/en/article/959/training-agents-to-walk-with-purpose%E2%80%8B
Akujuobi, U., Zhang, Q., Yufei, H. & Zhang, X. (2020). Recurrent attention walk for semi-supervised classification. Proceedings of the 13th International Conference on Web Search and Data Mining Houston TX USA, January 2020, 16-24.| article