{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/digital-electronics-and-analog-photonics-for","title":"Digital Electronics and Analog Photonics for Convolutional Neural Networks (DEAP-CNNs)","arxiv_id":"1907.01525","date":"2019-04-23","proceeding":null,"authors":["Viraj Bangari","Bicky A. Marquez","Heidi B. Miller","Alexander N. Tait","Mitchell A. Nahmias","Thomas Ferreira de Lima","Hsuan-Tung Peng","Paul R. Prucnal","Bhavin J. Shastri"],"abstract":"Convolutional Neural Networks (CNNs) are powerful and highly ubiquitous tools for extracting features from large datasets for applications such as computer vision and natural language processing. However, a convolution is a computationally expensive operation in digital electronics. In contrast, neuromorphic photonic systems, which have experienced a recent surge of interest over the last few years, propose higher bandwidth and energy efficiencies for neural network training and inference. Neuromorphic photonics exploits the advantages of optical electronics, including the ease of analog processing, and busing multiple signals on a single waveguide at the speed of light. Here, we propose a Digital Electronic and Analog Photonic (DEAP) CNN hardware architecture that has potential to be 2.8 to 14 times faster while maintaining the same power usage of current state-of-the-art GPUs.","url_abs":"https://arxiv.org/abs/1907.01525v1","url_pdf":"https://arxiv.org/pdf/1907.01525v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"digital-electronics-and-analog-photonics-for","repo_url":"https://github.com/Shastri-Lab/DEAP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}