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.
Thomas Van Vaerenbergh received the master's degree in applied physics and the Ph.D. degree in photonics from Ghent University, Ghent, Belgium, in 2010 and 2014, respectively. He was awarded the scientific prize Alcatel-Lucent Bell/FWO for his PhD thesis on all-optical spiking neurons in silicon photonics. In 2014, he joined the Palo Alto-based division of the Large-Scale Integrated Photonics team in Hewlett Packard Labs, part of Hewlett Packard Enterprise (HPE). Since 2019, he has been incubating a research team in HPE Belgium, expanding HPE’s research activities related to photonics and AI in the EMEA region. His main research interests include optical computing, analog photonic and electronic accelerators for combinatorial optimization, the modeling and design of passive silicon photonic devices, such as microring resonators and grating couplers, and the usage of state-of-the-art machine learning techniques to facilitate the design of photonic devices, circuits and devices.