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As nanoelectronics approaches the nanometer scale, a massive effort is underway to identify the next scalable logic technology beyond Complementary Metal Oxide Semiconductor (CMOS) computing. Such computing technology needs to improve…
Research has shown that convolutional neural networks contain significant redundancy, and high classification accuracy can be obtained even when weights and activations are reduced from floating point to binary values. In this paper, we…
Superconducting circuits have emerged as a promising platform to build quantum processors. The challenge of designing a circuit is to compromise between realizing a set of performance metrics and reducing circuit complexity and noise…
In this paper, a set of sensors is constructed via the pinning observability approach with the help of observability criteria given in [1] and [2], in order to make the given Boolean network (BN) be observable. Given the assumption that…
Quantum systems combining indistinguishable photon generation and spin-based quantum information processing are essential for remote quantum applications and networking. However, identification of suitable systems in scalable platforms…
Aiming to improve the checkerboard corner detection robustness against the images with poor quality, such as lens distortion, extreme poses, and noise, we propose a novel detection algorithm which can maintain high accuracy on inputs under…
We propose a spin-selective coherent electron transfer in a silicon-quantum-dot array. Oscillating magnetic fields and temporally controlled gate voltages are utilised to separate the electron wave function into different quantum dots…
As computing resource demands continue to escalate in the face of big data, cloud-connectivity and the internet of things, it has become imperative to develop new low-power, scalable architectures. Neuromorphic photonics, or photonic neural…
All-spin logic device (ASLD) has attracted increasing interests as one of the most promising post-CMOS device candidates, thanks to its low power, non-volatility and logic-in-memory structure. Here we investigate the key current-limiting…
The high demand for machine intelligence of doubling every three months is driving novel hardware solutions beyond charging of electrical wires given a resurrection to application specific integrated circuit (ASIC)-based accelerators. These…
We describe how quantum information may be transferred from photon polarization to electron spin in a semiconductor device. The transfer of quantum information relies on selection rules for optical transitions, such that two superposed…
In this paper, we introduce a new deep learning framework for discovering the phase field models from existing image data. The new framework embraces the approximation power of physics informed neural networks (PINN), and the computational…
Quantum sensors based on spin defect ensembles have seen rapid development in recent years, with a wide array of target applications. Historically, these sensors have used optical methods to prepare or read out quantum states. However,…
Nonlinear optical processing of ambient natural light is highly desired in computational imaging and sensing applications. A strong optical nonlinear response that can work under weak broadband incoherent light is essential for this…
Over the past decade Spiking Neural Networks (SNN) have emerged as one of the popular architectures to emulate the brain. In SNN, information is temporally encoded and communication between neurons is accomplished by means of spikes. In…
This paper introduces a novel hybrid AI method combining H filtering and an adaptive linear neuron network for flicker component estimation in power distribution systems.The proposed method leverages the robustness of the H filter to…
Spins are prototypical systems with the potential to probe magnetic fields down to the atomic scale limit. Exploiting their quantum nature through appropriate sensing protocols allows to enlarge their applicability to fields not always…
In this paper, we design a polar decoding platform for diverse application scenarios that require low-cost and low-power communications. Specifically, prevalent polar decoders such as successive cancellation (SC), SC-list (SCL) and Fano…
The deployment of neural networks in vehicle platforms and wearable Artificial Intelligence-of-Things (AIOT) scenarios has become a research area that has attracted much attention. With the continuous evolution of deep learning technology,…
Nanomagnets driven by spin currents provide a natural implementation for a neuron and a synapse: currents allow convenient summation of multiple inputs, while the magnet provides the threshold function. The objective of this paper is to…