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Our paper on "Efficient Processing of Deep Neural Networks: A Tutorial and Survey" is the cover story for the December issue of Proceedings of the IEEE
#DeepLearning http://www.rle.mit.edu/eems/wp-content/uploads/2017/11/2017_pieee_dnn.pdf …pic.twitter.com/MesKWB7QtC
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Video and slides for our talk on "Efficient Computing for Deep Learning, AI and Robotics" for MIT's Deep Learning Seminar Series are now available online! https://www.youtube.com/watch?v=WbLQqPw_n88 …
#deeplearning#Robotics#AIhttps://twitter.com/lexfridman/status/1220445237295878147 …
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Enabling efficient computing is critical for the deployment of deep learning, AI and robotics. It was great to have the opportunity to discuss this in our talk for
@lexfridman's Deep Learning Seminar Series today. https://tinyurl.com/SzeMITDL2020 pic.twitter.com/WVuOFNFK0K
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Exciting to see a large number of deep learning processors being developed in academia & industry! Learn how to evaluate and compare them at our tutorial on "How to Understand and Evaluate Deep Learning Processors"
@ieee_isscc in San Francisco on Feb 16 http://isscc.org/program-2/tutorials/ …pic.twitter.com/6Y64aW2s9t
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Our
#NeurIPS2019 tutorial on "Efficient Processing of Deep Neural Networks: from Algorithms to Hardware Architectures" is now online! We discuss how to evaluate efficient approaches for processing DNNs and highlight the key questions one should askhttps://slideslive.com/38921492Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
EEMS Group @ MIT (PI: Vivienne Sze) proslijedio/la je Tweet
#NeurIPS2019 tutorial on efficient deep learning by Vivienne Sze (@eems_mit). The tutorial will focus on evaluation and metrics, rather than techniques, which is a great idea!pic.twitter.com/0IK1h6WgdY
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There's a huge number of papers on efficient processing at deep neural networks. In today's tutorial at
#neurips, we discuss how to evaluate these works. Slides are https://www.rle.mit.edu/eems/publications/tutorials/ …https://twitter.com/JeffDean/status/1135114657344237568 …
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Peter receives the Gold medal in the ACM Student Research Competition (SRC) at MICRO-52 for his work on “A Mutual Information Accelerator for Autonomous Robot Exploration” (an extension of the RSS2019 paper). Check out his poster at http://www.rle.mit.edu/eems/wp-content/uploads/2019/10/Peter_ACM_SRC_poster.pdf …pic.twitter.com/2JAMFlEYfx
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If you are
@ICCAD, and interested in learning about fast energy estimation for designing and evaluating domain-specific accelerators (e.g., Eyeriss for deep neural networks), we invite you to check out Nellie's talk on Accelergy this afternoon in session 7B at#ICCAD2019!https://twitter.com/eems_mit/status/1161364588341764096 …
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We will be giving a tutorial on "Efficient Processing of Deep Neural Network: from Algorithms to Hardware Architectures" at
@NeurIPSConf (Dec. 9). We will discuss methods to enable efficient processing of DNNs across the entire stack. https://neurips.cc/#DeepLearningpic.twitter.com/iRR75tHxpW
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If you are attending
#micro52, and interested in learning about tools for evaluating deep neural network accelerator designs, check out the timeloop/accelergy tutorial this afternoon!https://twitter.com/eems_mit/status/1171847264341348352 …
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Slides available at http://eyeriss.mit.edu/tutorial-icip.html …
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Full house at
@ICIP2019 tutorial on Efficient Image Processing with Deep Neural Networks. We covered techniques (eg. network architecture design, neural architecture search, designing w/ hardware in the loop) & applications (eg. depth est, seg, super-res). http://eyeriss.mit.edu/tutorial-icip.html …pic.twitter.com/U09oYl4Wqp
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Designing efficient DNNs is critical for AI deployment on mobile devices. NetAdapt automatically adapts & simplifies DNNs for a platform w/ few parameters to tune for ease of use. It was used for MobileNetV3 & FastDepth. Check out the code @ https://github.com/denru01/netadapt …
#DeepLearningpic.twitter.com/bENy70ipdz
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If you are attending
@MicroArchConf and interested in learning about tools for evaluating deep neural network accelerator designs, we invite you to check out our tutorial on Acclergy and Timeloop taught by@Joel_Emer. More info at http://accelergy.mit.edu/tutorial.html#MICRO2019#DeepLearningpic.twitter.com/tcDOL5z8Y2
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How can Agile and Open Hardware accelerate our design process for applications such as AI, Robotics and Video Compression? Check out our talk at the SIGARCH Visioning Workshop @
#ISCA2019 https://youtu.be/0mmg80f9FQI pic.twitter.com/NYOpcu3EBJ
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We will be giving a tutorial on "Efficient Image Processing with Deep Neural Networks" at the
@ICIP2019 in Tapei (Sept 22). We will discuss applications such as image classification, depth estimation, image segmentation, and super-resolution. Register @ http://2019.ieeeicip.org/?action=page6&id=1 …pic.twitter.com/J8EMAI4gL1
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Estimating energy consumption is a key step in designing domain-specific accelerators. Our latest work, Accelergy, performs energy estimations without requiring a complete hardware description allowing the fast exploration of the accelerator design space: http://accelergy.mit.edu/ pic.twitter.com/D544agN71O
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It was great to be part of the inaugural TEDxMIT! To learn more about how to bring AI to the palm of your hand to tackle challenges in robotics and health care, check out the talk at https://www.youtube.com/watch?v=Y0XGSnRrWiU …
#deeplearning#robotics#AI#alzheimer#parkinsons#DigitalHealth https://twitter.com/TEDxMIT/status/1158405944402989056 …pic.twitter.com/xN7pkQUGVP
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Accelerating Shannon Mutual Information is critical for high-speed robot exploration. Our high-throughput accelerator is two orders of magnitude faster than existing solutions @ >10x lower power. Check out our
#RSS2019 paper https://bit.ly/2OzSfUe Joint work with@SertacKaramanpic.twitter.com/ZRj5HNogJ8
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Doing exciting research at the intersection of Systems and Machine Learning? Submit your work to the Conference of Systems and Machine Learning. Deadline Sept 9! CFP at https://systemsandml.org/ Check out videos from previous iterations SysML18, SysML19 at https://bit.ly/30JCKdB pic.twitter.com/C6VlQbyRnd
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