no code implementations • 22 May 2023 • Yannan Nellie Wu, Po-An Tsai, Saurav Muralidharan, Angshuman Parashar, Vivienne Sze, Joel S. Emer
Due to complex interactions among various deep neural network (DNN) optimization techniques, modern DNNs can have weights and activations that are dense or sparse with diverse sparsity degrees.
no code implementations • 21 Sep 2022 • Vibhaalakshmi Sivaraman, Pantea Karimi, Vedantha Venkatapathy, Mehrdad Khani, Sadjad Fouladi, Mohammad Alizadeh, Frédo Durand, Vivienne Sze
We design Gemino, a new neural compression system for video conferencing based on a novel high-frequency-conditional super-resolution pipeline.
1 code implementation • 14 Jul 2022 • Vijay Gadepally, Gregory Angelides, Andrei Barbu, Andrew Bowne, Laura J. Brattain, Tamara Broderick, Armando Cabrera, Glenn Carl, Ronisha Carter, Miriam Cha, Emilie Cowen, Jesse Cummings, Bill Freeman, James Glass, Sam Goldberg, Mark Hamilton, Thomas Heldt, Kuan Wei Huang, Phillip Isola, Boris Katz, Jamie Koerner, Yen-Chen Lin, David Mayo, Kyle McAlpin, Taylor Perron, Jean Piou, Hrishikesh M. Rao, Hayley Reynolds, Kaira Samuel, Siddharth Samsi, Morgan Schmidt, Leslie Shing, Olga Simek, Brandon Swenson, Vivienne Sze, Jonathan Taylor, Paul Tylkin, Mark Veillette, Matthew L Weiss, Allan Wollaber, Sophia Yuditskaya, Jeremy Kepner
Through a series of federal initiatives and orders, the U. S. Government has been making a concerted effort to ensure American leadership in AI.
no code implementations • 12 May 2022 • Yannan Nellie Wu, Po-An Tsai, Angshuman Parashar, Vivienne Sze, Joel S. Emer
This paper first presents a unified taxonomy to systematically describe the diverse sparse tensor accelerator design space.
1 code implementation • 1 Sep 2021 • Yi-Lun Liao, Sertac Karaman, Vivienne Sze
This naturally raises the question of how the performance of ViT can be advanced with design techniques of CNN.
no code implementations • CVPR 2021 • Tien-Ju Yang, Yi-Lun Liao, Vivienne Sze
Neural architecture search (NAS) typically consists of three main steps: training a super-network, training and evaluating sampled deep neural networks (DNNs), and training the discovered DNN.
no code implementations • 14 Dec 2020 • Hsin-Yu Lai, Gladynel Saavedra-Pena, Charles G. Sodini, Thomas Heldt, Vivienne Sze
We aid in neurocognitive monitoring outside the hospital environment by enabling app-based measurements of visual reaction time (saccade latency) and error rate in a cohort of subjects spanning the adult age spectrum.
no code implementations • 2 Feb 2020 • James Noraky, Vivienne Sze
When evaluated using RGB-D datasets of various dynamic scenes, our approach estimates depth maps with a mean relative error of 2. 5% while reducing the active depth sensor usage by over 90%.
no code implementations • 18 Dec 2019 • Tien-Ju Yang, Vivienne Sze
This paper describes various design considerations for deep neural networks that enable them to operate efficiently and accurately on processing-in-memory accelerators.
no code implementations • 29 Mar 2019 • Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael. I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar
Machine learning (ML) techniques are enjoying rapidly increasing adoption.
1 code implementation • 8 Mar 2019 • Diana Wofk, Fangchang Ma, Tien-Ju Yang, Sertac Karaman, Vivienne Sze
In this paper, we address the problem of fast depth estimation on embedded systems.
no code implementations • 13 Feb 2019 • Tien-Ju Yang, Maxwell D. Collins, Yukun Zhu, Jyh-Jing Hwang, Ting Liu, Xiao Zhang, Vivienne Sze, George Papandreou, Liang-Chieh Chen
We present a single-shot, bottom-up approach for whole image parsing.
Ranked #32 on Panoptic Segmentation on Cityscapes val
1 code implementation • 10 Jul 2018 • Yu-Hsin Chen, Tien-Ju Yang, Joel Emer, Vivienne Sze
In this work, we present Eyeriss v2, a DNN accelerator architecture designed for running compact and sparse DNNs.
4 code implementations • ECCV 2018 • Tien-Ju Yang, Andrew Howard, Bo Chen, Xiao Zhang, Alec Go, Mark Sandler, Vivienne Sze, Hartwig Adam
This work proposes an algorithm, called NetAdapt, that automatically adapts a pre-trained deep neural network to a mobile platform given a resource budget.
no code implementations • 27 Mar 2017 • Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, Joel Emer
The reader will take away the following concepts from this article: understand the key design considerations for DNNs; be able to evaluate different DNN hardware implementations with benchmarks and comparison metrics; understand the trade-offs between various hardware architectures and platforms; be able to evaluate the utility of various DNN design techniques for efficient processing; and understand recent implementation trends and opportunities.
no code implementations • 17 Mar 2017 • Amr Suleiman, Yu-Hsin Chen, Joel Emer, Vivienne Sze
Computer vision enables a wide range of applications in robotics/drones, self-driving cars, smart Internet of Things, and portable/wearable electronics.
1 code implementation • 22 Dec 2016 • Vivienne Sze, Yu-Hsin Chen, Joel Emer, Amr Suleiman, Zhengdong Zhang
Machine learning plays a critical role in extracting meaningful information out of the zetabytes of sensor data collected every day.
no code implementations • CVPR 2017 • Tien-Ju Yang, Yu-Hsin Chen, Vivienne Sze
With the proposed pruning method, the energy consumption of AlexNet and GoogLeNet are reduced by 3. 7x and 1. 6x, respectively, with less than 1% top-5 accuracy loss.
no code implementations • 27 Jul 2016 • Amr Suleiman, Zhengdong Zhang, Vivienne Sze
This paper presents a programmable, energy-efficient and real-time object detection accelerator using deformable parts models (DPM), with 2x higher accuracy than traditional rigid body models.
no code implementations • 29 Mar 2016 • Zhengdong Zhang, Vivienne Sze
State-of-the-art super-resolution (SR) algorithms require significant computational resources to achieve real-time throughput (e. g., 60Mpixels/s for HD video).