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Deep Learning: It’s Not All About Recognizing Cats and Dogs

Authors: Carole-Jean Wu, David Brooks, Udit Gupta, Hsien-Hsin Lee, and Kim Hazelwood
Date: November 2019

Recommendation systems form the backbone of most internet services: search engines use recommendation to order results, social networks to suggest friends and content, shopping websites to suggest purchases, and video streaming services to recommend movies [Facebook, Google, Alibaba, YouTube]. Recent publications show that an important class of Facebook’s recommendation use cases require more than 10x the datacenter inference capacity compared to common computer vision and NLP tasks. In fact, major categories of recommendation models account for over 70% of all AI inference cycles in Facebook’s production datacenter. In addition to their importance, DNN-based personalized recommendation models porcess both continuous and categorical input features leading to unique performance bottlenecks compared to CNNs and RNNs.


Designing AI-Enabled Technology for Society

Authors: Udit Gupta, Lillian Pentecost
Date: October 2018

Al-Enabled technology surrounds us in everyday life — from Face ID on an iPhoneX to Google searches and tailored advertisements sent from the cloud. This means AI is implemented everywhere — from smart phones to data centers all over the globe. How are these devices designed to support AI, and how does this change our daily interactions with technology? In this talk, we will use three examples (intelligent personal assistants, serving online search requests, and medical imaging), to discuss how AI is implemented and its impact on how we interact with technology.


Software-Programmable FPGAs

Authors: Udit Gupta
Date: June 2016

Modern workloads demand higher computational capabilities at low power consumption and cost. As traditional multi-core machines do not meet the growing computing requirements, architects are exploring alternative approaches. One solution is hardware specialization in the form of application specific integrated circuits (ASICs) to perform tasks at higher performance and lower power than software implementations. The cost of developing custom ASICs, however, remains high. Reconfigurable computing fabrics, such as field-programmable gate arrays (FPGAs), offer a promising alternative to custom ASICs. FPGAs couple the benefits of hardware acceleration with flexibility and lower cost.