Recurrent Neural Networks (RNNs) are used in applications that learn dependencies in data
sequences, such as speech recognition, human activity recognition, and anomaly detection …

Domain-specific machine learning (ML) accelerators such as Google's TPU and Apple's
Neural Engine now dominate CPUs and GPUs for energy-efficient ML processing. However …

We propose the photonic architecture based on microring resonator (MRR) arrays for binary
convolutional neural networks (BCNNs) accelerated computing. The MRR crossbar unit is …

Emerging AI applications such as ChatGPT, graph convolutional networks, and other deep
neural networks require massive computational resources for training and inference …

Advanced computing systems have long been enablers for breakthroughs in Machine
Learning (ML) algorithms either through sheer computational power or form-factor …

In the rapidly evolving landscape of artificial intelligence., large language models (LLMs)
and graph processing have emerged as transformative technologies for natural language …

This paper proposes a high-performance and energy-efficient optical near-sensor
accelerator for vision applications, called Lightator. Harnessing the promising efficiency …

Emerging AI applications such as large language models, vision transformers, graph
convolutional networks, and other deep neural networks require massive computational …

Several analog photonic architectures based on microring resonators (MRRs) have been
proposed to accelerate general matrix-matrix multiplications (GEMMs) that compose deep …

Silicon Photonic Neural Network Accelerators: Opportunities and Challenges Page 1
Silicon Photonic Neural Network Accelerators: Opportunities and Challenges Mahdi …