Transparent Hardware Acceleration For Deep Learning

When:
Thursday, Sep 28, 2017 4:00p -
5:00p

Where:
Venture Cafe Kendall
5th Floor
Cambridge, MA 02148

EventScheduled OfflineEventAttendanceMode

Admission:
FREE

Categories:
Business, Innovation, Lectures & Conferences, Meetup, Tech

Event website:
www.vencaf.org/roboticsconnect


Deep learning techniques are finding significant commercial success in a wide variety of industries. Built on several open-source frameworks such as Tensorflow and Caffe, deep learning capabilities are easy to incorporate into web applications. Jupyter notebook examples, available in open source, can get novice data scientists and engineers up to speed on new neural network architectures, pre-trained models, and public datasets. Large unstructured data sets such as images and videos are perfect input for deep learning methods. However, such types of data impose a lot of demands on computing resources. New types of hardware architectures such as GPUs and faster interconnects (e.g. NVLink) available in OpenPOWER systems are enabling practical speedups for deep learning. This session will show some software and system architectures and live demos showing how developers can easily incorporate deep learning capabilities on accelerated hardware using open source components such as Python, Docker, and Kubernetes.


Presenter: Indrajit Poddar
Senior Technical Staff Member, IBM

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09/28/2017 16:00:00 09/28/2017 17:00:00 America/New_York Transparent Hardware Acceleration For Deep Learning Deep learning techniques are finding significant commercial success in a wide variety of industries. Built on several open-source frameworks such as Tensorflow and Caffe, deep learning capabili...

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