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In high-performance RF photonic systems, the Electro-Optic (EO) modulators play a critical role as a key component, requiring low SWaP-C and high linearity. While traditional lithium niobate (LiNbO$_3$) Mach-Zehnder Modulators (MZMs) have…
Attention matrices are fundamental to transformer research, supporting a broad range of applications including interpretability, visualization, manipulation, and distillation. Yet, most existing analyses focus on individual attention heads…
Electro-optic modulators (EOMs) are vital for optical imaging and information processing, with free-space devices enabling LiDAR and beam control. Lithium niobate (LN), powered by the strong Pockels effect and scalable LN-on-insulator…
Optical modulators are at the heart of optical communication links. Ideally, they should feature low insertion loss, low drive voltage, large modulation bandwidth, high linearity, compact footprint and low manufacturing cost. Unfortunately,…
The attention mechanism is the primary component of the transformer architecture; it has led to significant advancements in deep learning spanning many domains and covering multiple tasks. In computer vision, the attention mechanism was…
Transformers have become foundational architectures for both natural language and computer vision tasks. However, the high computational cost makes it quite challenging to deploy on resource-constraint devices. This paper investigates the…
Vision transformers (ViTs) have pushed the state-of-the-art for visual perception tasks. The self-attention mechanism underpinning the strength of ViTs has a quadratic complexity in both computation and memory usage. This motivates the…
The stability of the integrated photonic circuits is of critical importance for many applications that require high frequency precision or robust operation over time, such as optomechanical sensing, frequency conversion, optical…
Manipulating the frequency and bandwidth of nonclassical light is essential for implementing frequency-encoded/multiplexed quantum computation, communication, and networking protocols, and for bridging spectral mismatch among various…
Programmable spatiotemporal control of light is crucial for advancements in optical communications, imaging, and quantum technologies. Commercial spatial light modulators (SLMs) typically have megapixel-scale apertures but are limited to…
Vision transformers (ViTs) have pushed the state-of-the-art for various visual recognition tasks by patch-wise image tokenization followed by self-attention. However, the employment of self-attention modules results in a quadratic…
Transformers are mostly relying on softmax attention, which introduces quadratic complexity with respect to sequence length and remains a major bottleneck for efficient inference. Prior work on linear or hybrid attention typically replaces…
Thin-film lithium niobate (TFLN) has played a pivotal role in the advancement of integrated photonics, by supporting a diverse range of applications including nonlinear optics, electro-optics, and piezo-optomechanics. The effective…
Transformers are a widespread and successful model architecture, particularly in Natural Language Processing (NLP) and Computer Vision (CV). The essential innovation of this architecture is the Attention Mechanism, which solves the problem…
We present TransNormerLLM, the first linear attention-based Large Language Model (LLM) that outperforms conventional softmax attention-based models in terms of both accuracy and efficiency. TransNormerLLM evolves from the previous linear…
Modern communication networks require high performance and scalable electro-optic modulators that convert electrical signals to optical signals at high speed. Existing lithium niobate modulators have excellent performance but are bulky and…
Transformers have become one of the dominant architectures in deep learning, particularly as a powerful alternative to convolutional neural networks (CNNs) in computer vision. However, Transformer training and inference in previous works…
The attention mechanism is a pivotal element within the transformer architecture, making a substantial contribution to its exceptional performance. Within this attention mechanism, Softmax is an imperative component that enables the model…
The attention mechanism is the key to the success of transformers in different machine learning tasks. However, the quadratic complexity with respect to the sequence length of the vanilla softmax-based attention mechanism becomes the major…
Superconducting cavity electro-optics (EO) presents a promising route to coherently convert microwave and optical photons and distribute quantum entanglement between superconducting circuits over long-distance through an optical network.…