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A high efficiency hardware integration of neural networks benefits from realizing nonlinearity, network connectivity and learning fully in a physical substrate. Multiple systems have recently implemented some or all of t...

Plant disease diagnosis in field settings is challenged by subtle symptomology, high inter-class visual similarity, and class imbalance, making automated detection particularly difficult. While deep learning models achie...

Modular architectures offer a scalable path toward fault-tolerant quantum computing by interconnecting smaller quantum processing units provided that high-rate, fault-tolerant interfaces can be realized across modules. W...

This paper addresses the growing need for implementing intelligent Natural Language Processing (NLP) systems on low-power, memory-limited devices such as Raspberry Pi, mobile phones, and IoT edge hardware. As edge comput...

ABSTRACT The growing use of neural networks in privacy‐sensitive applications necessitates architectures that inherently protect both data and model integrity. We present a model‐inversion‐resistant physical unclon...

In this paper, we combine a Content-Addressable Memory (CAM) encoding scheme previously proposed for analog electronic CAMs (E-CAMs) with optical multiplexing techniques to create two new photonic CAM architectures—wav...

Machine learning (ML) and neuromorphic computing have been enforcing problem-solving in many applications. Such approaches found fertile ground in optical communications, a technological field that is very demanding in t...

Solar peer-to-peer (P2P) energy trading is a promising approach that allows prosumers and consumers to exchange energy directly, which reduces dependence on the centralized power grid. However, implementing such a P2P en...