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Friday March 24th, 12:00 pm in Henley Hall 1010 New machine learning algorithms such as deep neural networks and the availability of large datasets have created a large drive towards new types of hardware capable of executing these algorithms with higher energy-efficiency. Recently, silicon photonics has emerged as a promising hardware platform for neuromorphic computing due to its inherent capability to process linear and non-linear operations and transmit a high bandwidth of data in parallel. At Hewlett Packard Labs, an energy-efficient dense-wavelength division multiplexing (DWDM) silicon photonics platform has been developed as the underlying foundation for innovative neuromorphic computing architectures. The latest research on our silicon photonic neuromorphic platform will be presented and discussed.  From An Energy-Efficient and Bandwidth-Scalable DWDM Heterogeneous Silicon Photonics Integration Platform -- https://doi.org/10.1109/JSTQE.2022.3181939 Biography:
Bassem Tossoun received his PhD in Electrical Engineering at the University of Virginia in 2019 with his research interests including silicon photonics and the design, fabrication, and characterization of optoelectronic devices for data communications. Currently, he is a Senior Research Scientist at Hewlett Packard Labs working on heterogeneously integrated III-V on silicon photonic devices for next-generation optical computing and communications. Comments are closed.
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Founded in 2018, Nexus Photonics has developed integrated photonics ready to scale. Smaller, lighter and faster, their platform outperforms industry benchmarks, and operates in an ultra-broadband wavelength range from ultraviolet to infrared to support a wide breadth of practical applications.
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