activity
20202026
most citedCharacterization and Optimization of Integrated Silicon-Photonic Neural Networks under Fabrication-Process Variations

32 citations · 107 across the 32 of their papers we have counts for

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16 papers · 1 filter

cs.ET2025★ 18 cited

Roadmap on Neuromorphic Photonics

Daniel Brunner, Bhavin J. Shastri, Mohammed A. Al Qadasi +147

This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementatio…

cs.ET2023★ 2 cited

Compact and Low-Loss PCM-based Silicon Photonic MZIs for Photonic Neural Networks

Amin Shafiee, Sanmitra Banerjee, Benoit Charbonnier +2

We present an optimized Mach-Zehnder Interferometer (MZI) with phase change materials for photonic neural networks (PNNs). With 0.2 dB loss, -38 dB crosstalk, and length of 52 micr…

cs.ET2023

SerIOS: Enhancing Hardware Security in Integrated Optoelectronic Systems

Felipe Gohring de Magalhaes, Mahdi Nikdast, Gabriela Nicolescu

Silicon photonics (SiPh) has different applications, from enabling fast and high-bandwidth communication for high-performance computing systems to realizing energy-efficient optica…

cs.ET2023★ 1 cited

Analysis of Optical Loss and Crosstalk Noise in MZI-based Coherent Photonic Neural Networks

Amin Shafiee, Sanmitra Banerjee, Krishnendu Chakrabarty +2

With the continuous increase in the size and complexity of machine learning models, the need for specialized hardware to efficiently run such models is rapidly growing. To address…

cs.ET2023★ 1 cited

Design Space Exploration for PCM-based Photonic Memory

Amin Shafiee, Benoit Charbonnier, Sudeep Pasricha +1

The integration of silicon photonics (SiPh) and phase change materials (PCMs) has created a unique opportunity to realize adaptable and reconfigurable photonic systems. In particul…

cs.ET2022★ 27 cited

Characterizing Coherent Integrated Photonic Neural Networks under Imperfections

Sanmitra Banerjee, Mahdi Nikdast, Krishnendu Chakrabarty

Integrated photonic neural networks (IPNNs) are emerging as promising successors to conventional electronic AI accelerators as they offer substantial improvements in computing spee…