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Most of computing’s carbon emissions are coming from manufacturing and infrastructure

Authors: Carole-Jean Wu, Udit Gupta
Date: March 2021

Reducing computing’s carbon footprint isn’t all about optimizing hardware and software.

When it comes to reducing carbon emissions, tech companies have started considering their complete carbon footprint. Since companies have stronger operational control over their own facilities and energy procurement, many of them have spent the last decade focusing on reducing their emissions related to operational energy consumption (opex) and setting carbon neutral or net zero operational goals. But as more companies are approaching their 100 percent renewable energy targets, they’ve started looking into emissions related to their value chain or capital energy consumption (capex) — indirect emissions that come from hardware manufacturing and infrastructure.

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Optimizing infrastructure for neural recommendation at scale

Authors: Carole-Jean Wu, Udit Gupta
Date: March 2020

We are sharing an in-depth characterization and analysis for infrastructures used to deliver personalized results in deep neural network-based (DNN) recommendation at scale. Although DNNs are often used to help generate search results, to provide content suggestions, and for other common applications for internet services, relatively little research attention has been devoted to optimizing system infrastructures to serve such recommendations at scale. In addition to sharing insights about how this important class of neural recommendation models performs at production scale, we’ve also released the open source workloads and related performance metrics that we used, to help other researchers and engineers to evaluate their DNNs.

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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.

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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.

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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.

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