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A Mobile Accelerator Architecture for Ray Tracing

A Mobile Accelerator Architecture for Ray Tracing

A Mobile Accelerator Architecture for Ray Tracing Josef Spjut, Daniel Kopta, Erik Brunvand, and Al Davis Abstract—Mobile computing in the form of smart phones and tablets is becoming ubiquitous. As these devices are being used for increasingly sophisticated tasks, the graphics requirements are also increasing. With the growing desire for highly realistic graphics, the use of ray tracing for ...

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A Mobile Accelerator Architecture for Ray Tracing

A Mobile Accelerator Architecture for Ray Tracing

A Mobile Accelerator Architecture for Ray Tracing Josef Spjut, Daniel Kopta, Erik Brunvand and Al Davis sjosef, dkopta, elb, ald <AT> utah.edu 1. School of Computing University of Utah State of the Art in Mobile Graphics 2. School of Computing University of Utah Mobile SoC 3. School of Computing University of Utah Mobile SoC 4. School of Computing University of Utah Scene Complexity 5 ...

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portals to mobile devices IBM Mobile Portal Accelerator V6 ...

portals to mobile devices IBM Mobile Portal Accelerator V6 ...

Architect V7.5.1 environments, and new mobile portal runtime, features and tooling enhancements. Accessibility by people with disabilities Mobile Portal Accelerator V6.1 provides the following accessibility features for the following environments: • Web applications – Summarizes the content of each graph and chart or uses the logdesc attribute to link to the description or data – When an ...

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ACCELERATOR ARCHITECTURES - Duke University

ACCELERATOR ARCHITECTURES - Duke University

mobile devices (to improve overall perfor-mance per watt). Scientific computing, oil and gas exploration, and financial modeling have also been strong markets in which the accelerator model has provided value, particularly as more computation is done on an interactive, client-side basis in order to drastically reduce delay to discovery or decision making. Why does the acceleration model work ...

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Neural Network Acceleration on Mobile Devices

Neural Network Acceleration on Mobile Devices

on Mobile Devices Dongyoung Kim Machine learning team Hyperconnect Seoul, South Korea [email protected] . Agenda Introduction Neural Processing Unit (NPU) NPU utilizing sparsity Zero-aware neural network accelerator (ZeNA) NPU utilizing reduced precision Outlier quantization and Precision highway Working as an AI System Architect in the Industry On-device Machine Learning Neural network ...

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A Compact 446 Gbps/W AES accelerator for Mobile SoC and ...

A Compact 446 Gbps/W AES accelerator for Mobile SoC and ...

A Compact 446 Gbps/W AES accelerator for Mobile SoC and IoT in 40nm . Yiqun Zhang, Kaiyuan Yang, Mehdi Saligane, David Blaauw, Dennis Sylvester . University of Michigan, Ann Arbor, MI Email: [email protected] . Abstract. An AES hardware accelerator targeting energy efficient, low cost mobile and IoT applications is fabricated in 40nm CMOS. The proposed design eliminates the ShiftRow stage in ...

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1 Software-Defined Design Space Exploration for an Efficient ...

1 Software-Defined Design Space Exploration for an Efficient ...

accelerator may not be large enough to hold the entire data set for input feature maps, kernel weights, and output feature maps. Therefore, they need to be partitioned into smaller data chunks in order to fit in the on-chip memory. This is called loop tiling [30]. T* parameters (e.g., Tof) denote the corresponding design variables for loop tiling. They represent the size of the data chunks ...

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Nortel Mobile Client Accelerator 3.3 Integration Guide

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Mobile Client Accelerator server, any Nortel Mobile Client Accelerator client that logs into the server will receive this update and apply this routing table rule to all outgoing new TCP connections from the Client Machine. Nortel Mobile Client Accelerator 3.3 Integration Guide Nortel Mobile Client Accelerator Release 3.3 Integration Guide NN47224-300 01.01 Standard February 4, 2008 10 ...

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AWS Architecture Monthly January 2020

AWS Architecture Monthly January 2020

George Mao is a Specialist Solutions Architect at Amazon Web Services, focused on the Serverless platform. He is responsible for helping customers design and operate Serverless applications using services like AWS Lambda, Amazon API Gateway, Amazon Cognito, and Amazon DynamoDB. George is a software engineer and enjoys contributing to open source projects, delivering technical presentations at ...

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Research Laboratory of Electronics at MIT

Research Laboratory of Electronics at MIT

Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices Yu-Hsin Chen, Student Member, IEEE, Tien-Ju Yang, Student Member, IEEE, Joel Emer, Fellow, IEEE, and Vivienne Sze, Senior Member, IEEE Abstract—A recent trend in deep neural network (DNN) development is to extend the reach of deep learning applications to platforms that are more resource and energy ...

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View on 5G Architecture

View on 5G Architecture

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