Optical Computing for Deep Neural Network Acceleration: Foundations, Recent Developments, and Emerging Directions
S Pasricha - arXiv preprint arXiv:2407.21184, 2024 - arxiv.org
Emerging artificial intelligence applications across the domains of computer vision, natural
language processing, graph processing, and sequence prediction increasingly rely on deep …
language processing, graph processing, and sequence prediction increasingly rely on deep …
OPIMA: Optical Processing-in-Memory for Convolutional Neural Network Acceleration
Recent advances in machine learning (ML) have spotlighted the pressing need for
computing architectures that bridge the gap between memory bandwidth and processing …
computing architectures that bridge the gap between memory bandwidth and processing …
Programmable phase change materials and silicon photonics co-integration for photonic memory applications: a systematic study
A Shafiee, B Charbonnier, J Yao… - Journal of Optical …, 2024 - spiedigitallibrary.org
The integration of phase change materials (PCMs) with photonic devices creates a unique
opportunity for realizing application-specific, reconfigurable, and energy-efficient photonic …
opportunity for realizing application-specific, reconfigurable, and energy-efficient photonic …
Hardware-Software Codesign of Silicon Photonic AI Accelerators
FP Sunny - 2024 - search.proquest.com
Abstract Machine learning applications have become increasingly prevalent over the past
decade across many real-world use cases, from smart consumer electronics to automotive …
decade across many real-world use cases, from smart consumer electronics to automotive …