Related papers: Pseudo-Random Generator based on a Photonic Neurom…
Advances in silicon photonics technology have enabled the field of neuromorphic photonics, where analog neuron-like processing elements are implemented in silicon photonics technology. Accurate and scalable simulation tools for photonic…
Random numbers are essential for our modern information based society e.g. in cryptography. Unlike frequently used pseudo-random generators, physical random number generators do not depend on complex algorithms but rather on a physical…
This paper provides a proof of concept for using SRAM based Physically Unclonable Functions (PUFs) to generate private keys for IoT devices. PUFs are utilized, as there is inadequate protection for secret keys stored in the memory of the…
Neuromorphic processors improve the efficiency of machine learning algorithms through the implementation of physical artificial neurons to perform computations. However, whilst efficient classical neuromorphic processors have been…
Pseudo-random number generators (PRNGs) play an important role to ensure the security and confidentiality of image cryptographic algorithms. Their primary function is to generate a sequence of numbers that possesses unpredictability and…
Randomness in optical systems emerges as a powerful resource for generating complex, non-deterministic light-matter interactions. In particular, random plasmonic metasurfaces harness nanoscale disorder to produce unique and irreproducible…
Weak physical uncloneable function (WPUF) encryption key means that the manufacturer of the hardware can clone the key but anybody else is unable to so that. Strong physical uncloneable function (SPUF) encryption key means that even the…
Cellular nonlinear network (CNN) provides an infrastructure for Cellular Automata to have not only an initial state but an input which has a local memory in each cell with much more complexity. This property has many applications which we…
In recent years, neuromorphic computing and spiking neural networks (SNNs) have ad-vanced rapidly through integration with deep learning. However, the performance of SNNs still lags behind that of convolutional neural networks (CNNs),…
Most atomic physics experiments are controlled by a digital pattern generator used to synchronize all equipment by providing triggers and clocks. Recently, the availability of well-documented open-source development tools has lifted the…
Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for…
We propose a method to realize a robust quantum random number generator based on bosonic stimulation. A particular implementation that employs weak coherent pulses and conventional avalanche photo-diode detectors (APDs) is discussed.
Unique key generation is essential for encryption purposes between Internet of Things (IoT) devices. To produce a unique key for this encryption, Physical Unclonable Functions (PUFs) might be employed. Also, the Random Number Generator…
Random number generators are essential to ensure performance in information technologies, including cryptography, stochastic simulations and massive data processing. The quality of random numbers ultimately determines the security and…
Spiking Neural Networks (SNNs) offer an event-driven and more biologically realistic alternative to standard Artificial Neural Networks based on analog information processing. This can potentially enable energy-efficient hardware…
Photonic qubits should be controllable on-chip and noise-tolerant when transmitted over optical networks for practical applications. Furthermore, qubit sources should be programmable and have high brightness to be useful for quantum…
All-optical devices are essential for next generation ultrafast, ultralow-power and ultrahigh bandwidth information processing systems. Silicon microring resonators (SiMRR) provide a versatile platform for all-optical switching and…
Adaptable, reconfigurable and programmable are key functionalities for the next generation of silicon-based photonic processors, neural and quantum networks. Phase change technology offers proven non-volatile electronic programmability,…
Neural networks have enabled applications in artificial intelligence through machine learning, and neuromorphic computing. Software implementations of neural networks on conventional computers that have separate memory and processor (and…
Growing interest in quantum computing for practical applications has led to a surge in the availability of programmable machines for executing quantum algorithms. Present day photonic quantum computers have been limited either to…