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PhotoFourier: A Photonic Joint Transform Correlator-Based Neural Network Accelerator
The last few years have seen a lot of work to address the challenge of low-latency and high-throughput convolutional neural network inference.
High Throughput Multi-Channel Parallelized Diffraction Convolutional Neural Network Accelerator
Convolutional neural networks are paramount in image and signal processing including the relevant classification and training tasks alike and constitute for the majority of machine learning compute demand today.
Silicon Photonic Architecture for Training Deep Neural Networks with Direct Feedback Alignment
There has been growing interest in using photonic processors for performing neural network inference operations; however, these networks are currently trained using standard digital electronics.
All-Chalcogenide Programmable All-Optical Deep Neural Networks
Deeplearning algorithms are revolutionising many aspects of modern life.
Autonomous Vehicles
Decision Making
Optics
Emerging Technologies
Roadmap on Integrated Quantum Photonics
The reduction in size, weight, power, and improvement in stability that will be enabled by QPICs will play a key role in increasing the degree of complexity and scale in quantum demonstrations.
Quantum Physics
Strain Induced Modulation of Local Transport of 2D Materials at the Nanoscale
Such spatially-textured electronic behavior via surface topography induced strain variations in atomistic-layered materials at the nanoscale opens up new opportunities to control fundamental material properties and offers a myriad of design and functional device possibilities for electronics, nanophotonics, flextronics, or smart cloths.
Mesoscale and Nanoscale Physics Applied Physics
Photonic tensor cores for machine learning
With an ongoing trend in computing hardware towards increased heterogeneity, domain-specific co-processors are emerging as alternatives to centralized paradigms.
A Winograd-based Integrated Photonics Accelerator for Convolutional Neural Networks
Among different types of neural networks, convolutional neural networks (CNNs) have been widely adopted as they have achieved leading results in many fields such as computer vision and speech recognition.
PCNNA: A Photonic Convolutional Neural Network Accelerator
Convolutional Neural Networks (CNN) have been the centerpiece of many applications including but not limited to computer vision, speech processing, and Natural Language Processing (NLP).
Identifying Mirror Symmetry Density with Delay in Spiking Neural Networks
The ability to rapidly identify symmetry and anti-symmetry is an essential attribute of intelligence.
