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  • Even Faster Convolutions: Winograd Convolutions meet Integer Quantization and Architecture Search

    Javier Fernandez-Marques
    Javier Fernandez-Marques

    Javier Fernandez-Marques recently completed his internship with us at Arm ML Research Lab, working with the Machine Learning team in Cambridge UK, and in Boston, US. During his four months, Javier’s research focused on Winograd convolutions, the very…

    • 1 month ago
    • Arm Research
    • Research Articles
  • SCALE-Sim: A cycle-accurate NPU simulator for your research experiments

    Paul Whatmough
    Paul Whatmough

    Architectural Simulators

    Architecture simulators of various kinds are a key tool in the computer architecture toolbox. They provide a convenient model of real hardware, such as a CPU or even a whole System on Chip (SoC), at a level of abstraction that…

    • 2 months ago
    • Arm Research
    • Research Articles
  • TinyML Applications Require New Network Architectures

    Urmish Thakker
    Urmish Thakker

    Why TinyML?

    Researchers have studied neural network compression for quite some time. However, the need for always-on compute has led to a recent trend towards executing these applications on even smaller IoT devices. These devices can have total system…

    • 4 months ago
    • Arm Research
    • Research Articles
  • TinyML Summit

    Charlotte Christopherson
    Charlotte Christopherson
    February 12, 2020 12:00 PM to February 13, 2020 01:00 PM Coordinated Universal Time
    San Jose, California
    Following the success of the inaugural tinyML Summit 2019 , the tinyML committee invites low power machine learning experts from the industry, academia, start-ups and government labs from all over the Globe to join the tinyML Summit 2020 to share the...
    • 4 months ago
    • Arm Research
    • Arm Research Events
  • A Year of Discovery: Arm Research 2019

    Rhiannon Burleigh
    Rhiannon Burleigh

    2019 was yet another year of incredible technology discovery. From healthcare, to food, to artificial intelligence1, exciting and inspiring advancements have been made across the research community, and Arm Research is no exception. Our teams have shared their…

    • 5 months ago
    • Arm Research
    • Research Articles
  • Sparse Architecture Search (SpArSe): Democratizing and Enabling TinyML on Arm M-class

    Igor Fedorov
    Igor Fedorov

    Microcontrollers (MCUs) are truly the ubiquitous computer of our time. They are tiny, cheap, and low power. They can often be powered indefinitely using a solar cell. They are in your watch, your fridge, and your car contains about 30 of them. The average…

    • 6 months ago
    • Arm Research
    • Research Articles
  • NeurIPS 2019

    Rhiannon Burleigh
    Rhiannon Burleigh
    December 08, 2019 09:00 AM to December 14, 2019 05:00 PM Coordinated Universal Time
    Vancouver, Canada
    Igor Fedorov , Senior Research Engineer, is presenting a poster titled ' SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers'. T he poster session will take place 12 December between 5:00pm and 7:00pm. NeurlIPS (Neural...
    • 6 months ago
    • Arm Research
    • Arm Research Events
  • Skipping RNN State-updates Without Retraining the Original Model

    Urmish Thakker
    Urmish Thakker

    Recurrent Neural Networks (RNNs) are an important class of algorithms. They are used in tasks where the strict order of the input conveys certain information, for example, natural language processing (NLP) and time-series based data. Increasingly, these…

    • 6 months ago
    • Arm Research
    • Research Articles
  • Collaboration Case Study: Machine Learning Hardware with Harvard University

    Paul Whatmough
    Paul Whatmough

    Partnerships are important to us at Arm. We are an ecosystem company, which means that we strive to work together with partner companies for mutual success. This philosophy extends to Arm Research, where partnerships allow us to extend our reach further…

    • 7 months ago
    • Arm Research
    • Research Articles
  • Giving a flexible edge to the IoT

    Charlotte Christopherson
    Charlotte Christopherson

    As the Internet of Things (IoT) continues to revolutionise our daily lives, the demand for smaller, smarter, and more diverse flexible technology has never been greater. Increasingly complex demands have driven the development of smart sensors to monitor…

    • over 1 year ago
    • Arm Research
    • Research Articles
  • Alpha-Blending: Quantizing networks without using the STE

    Matthew Mattina
    Matthew Mattina

    Increasingly, intelligent applications are using neural networks at their core to deliver new functionality to users. These applications include language understanding and translation, image recognition, and object tracking and localization. Furthermore…

    • 10 months ago
    • Arm Research
    • Research Articles
  • Taking Constrained ML to the Next Level

    Charlotte Christopherson
    Charlotte Christopherson

    The Arm ML Research Lab explores cutting edge techniques and state-of-the-art algorithms. One of our primary research thrusts is investigating ways to bring more machine learning applications to Arm's products, and make existing applications more efficient

    …
    • 10 months ago
    • Arm Research
    • Research Articles
  • Learning low-precision neural networks without Straight-Through Estimator(STE)

    Charlotte Christopherson
    Charlotte Christopherson
    Zhi-Gang Liu, Matthew Mattina
    The Straight-Through Estimator (STE) is widely used for back-propagating gradients through the quantization function, but the STE technique lacks a complete theoretical understanding. We propose an alternative methodology…
    • Learning low-precision neural networks without Straight-Through Estimator(STE).pdf
    • over 1 year ago
    • Arm Research
    • Resources
  • Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs

    Charlotte Christopherson
    Charlotte Christopherson
    Partha Maji, Andrew Mundy, Ganesh Dasika, Jesse Beu, Matthew Mattina, Robert Mullins
    The Winograd or Cook-Toom class of algorithms help to reduce the overall compute complexity of many modern deep convolutional neural networks (CNNs). Although there has been…
    • Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs.pdf
    • over 1 year ago
    • Arm Research
    • Resources
  • Measuring scheduling efficiency of RNNs for NLP applications

    Charlotte Christopherson
    Charlotte Christopherson
    Urmish Thakker, Ganesh Dasika, Jesse Beu, Matthew Mattina
    Recurrent neural networks (RNNs) have shown state of the art results for speech recognition, natural language processing, image captioning and video summarizing applications. Many of these applications…
    • Measuring scheduling efficiency of RNNs for NLP applications.pdf
    • over 1 year ago
    • Arm Research
    • Resources
  • FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning

    Charlotte Christopherson
    Charlotte Christopherson
    Paul N. Whatmough, Chuteng Zhou, Patrick Hansen, Shreyas Kolala Venkataramanaiah, Jae-sun Seo, Matthew Mattina
    The computational demands of computer vision tasks based on state-of-the-art Convolutional Neural Network (CNN) image classification far exceed…
    • FixyNN- Efficient Hardware for Mobile Computer Vision via Transfer Learning.pdf
    • over 1 year ago
    • Arm Research
    • Resources
  • Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications

    Charlotte Christopherson
    Charlotte Christopherson
    Dibakar Gope, Ganesh Dasika, Matthew Mattina
    Machine learning-based applications are increasingly prevalent in IoT devices. The power and storage constraints of these devices make it particularly challenging to run modern neural networks, limiting the…
    • Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications.pdf
    • over 1 year ago
    • Arm Research
    • Resources
  • Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision

    Charlotte Christopherson
    Charlotte Christopherson
    Yuhao Zhu, Anand Samajdar, Matthew Mattina, Paul Whatmough
    Continuous computer vision (CV) tasks increasingly rely on convolutional neural networks (CNN). However, CNNs have massive compute demands that far exceed the performance and energy constraints…
    • Euphrates- Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision.pdf
    • over 1 year ago
    • Arm Research
    • Resources
  • Mobile Machine Learning Hardware at Arm: A Systems-on-Chip (SoC) Perspective

    Charlotte Christopherson
    Charlotte Christopherson
    Yuhao Zhu, Matthew Mattina, Paul Whatmough
    Machine learning is playing an increasingly significant role in emerging mobile application domains such as AR/VR, ADAS, etc. Accordingly, hardware architects have designed customized hardware for machine learning…
    • Mobile Machine Learning Hardware at ARM- A Systems-on-Chip (SoC) Perspective.pdf
    • over 1 year ago
    • Arm Research
    • Resources
  • Energy Efficient Hardware for On-Device CNN Inference via Transfer Learning

    Charlotte Christopherson
    Charlotte Christopherson
    Paul Whatmough, Chuteng Zhou, Patrick Hansen, Matthew Mattina
    On-device CNN inference for real-time computer vision applications can result in computational demands that far exceed the energy budgets of mobile devices. This paper proposes FixyNN, a co-designed…
    • Energy Efficient Hardware for On-Device CNN Inference via Transfer Learning.pdf
    • over 1 year ago
    • Arm Research
    • Resources
  • SCALE-Sim: Systolic CNN Accelerator Simulator

    Charlotte Christopherson
    Charlotte Christopherson
    Ananda Samajdar, Yuhao Zhu, Paul Whatmough, Matthew Mattina, Tushar Krishna
    Systolic Arrays are one of the most popular compute substrates within Deep Learning accelerators today, as they provide extremely high efficiency for running dense matrix multiplications…
    • SCALE-Sim- Systolic CNN Accelerator Simulator.pdf
    • over 1 year ago
    • Arm Research
    • Resources
  • Efficient and Robust Machine Learning for Real-World Systems

    Charlotte Christopherson
    Charlotte Christopherson
    Franz Pernkopf, Wolfgang Roth, Matthias Zoehrer, Lukas Pfeifenberger, Guenther Schindler, Holger Froening, Sebastian Tschiatschek, Robert Peharz, Matthew Mattina, Zoubin Ghahramani
    While machine learning is traditionally a resource intensive task, embedded systems…
    • Efficient and Robust Machine Learning.pdf
    • over 1 year ago
    • Arm Research
    • Resources
  • Re-Work Deep Learning Summit

    Charlotte Christopherson
    Charlotte Christopherson
    May 23, 2019 08:00 AM to May 24, 2019 04:00 PM Coordinated Universal Time
    Boston
    Join Matthew Mattina, Senior Director of Machine Learning and AI Research at Arm for his talk at the Deep Learning Summit in Boston. ML on the Edge: Hardware and Models for Machine Learning on Constrained Platforms Deep neural networks are a key technology...
    • over 1 year ago
    • Arm Research
    • Arm Research Events
  • Efficient Hardware for Mobile Computer Vision via Transfer Learning

    Paul Whatmough
    Paul Whatmough

    Mobile computing is on the rise, and currently moving into some really exciting new applications and form factors ­– augmented reality (AR) glasses, unmanned aerial vehicles (UAVs), automated driver assistance systems (ADAS) in automobiles, and more.…

    • over 1 year ago
    • Arm Research
    • Research Articles
  • The Arm Research Workshop on Novel Algorithms

    Bo Eyole
    Bo Eyole

    Arm Research is responsible for delivering a clear vision of disruptive and emerging technologies and how they may affect our future. This disruptive technology landscape is used to develop our research strategy, which guides internal research, external…

    • over 1 year ago
    • Arm Research
    • Research Articles
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