14–17 Oct 2019

Schedule: Hardware sessions

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11:0511:45 Wednesday, 16 October 2019
Location: King's Suite - Sandringham
Yan Zhang (Microsoft), Mathew Salvaris (Microsoft)
Average rating: ***..
(3.67, 3 ratings)
When IoT meets AI, a new round of innovations begins. Yan Zhang and Mathew Salvaris examine the methodology, practice, and tools around deploying machine learning models on the edge. They offer a step-by-step guide to creating an ML model using Python, packaging it in a Docker container, and deploying it as a local service on an edge device as well as deployment on GPU-enabled edge devices. Read more.
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13:4514:25 Wednesday, 16 October 2019
Location: King's Suite - Balmoral
Paris Buttfield-Addison (Secret Lab), Tim Nugent (Lonely Coffee)
Average rating: ****.
(4.75, 8 ratings)
On-device ML and AI is the future for privacy-conscious, cloud-averse users of modern smartphones. Paris Buttfield-Addison and Tim Nugent explore what's possible using CoreML, Swift, and associated frameworks in tandem with the powerful ML-tuned silicon in modern Apple iOS hardware. They demonstrate and create ML and AI features with Swift to show how much you can do without touching the cloud. Read more.
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16:0016:40 Wednesday, 16 October 2019
Location: King's Suite - Sandringham
Thomas Phelan (HPE BlueData)
Average rating: ****.
(4.50, 2 ratings)
Today, organizations understand the need to keep pace with new technologies when it comes to performing data science with machine learning and deep learning, but these new technologies come with their own challenges. Thomas Phelan demonstrates the deployment of TensorFlow, Horovod, and Spark using the NVIDIA CUDA stack on Docker containers in a secure multitenant environment. Read more.
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16:5017:30 Wednesday, 16 October 2019
Location: King's Suite - Balmoral
Average rating: ****.
(4.25, 12 ratings)
Developing perception algorithms for autonomous vehicles is incredibly difficult, as they need to operate in thousands of driving conditions and locations. Adam Grzywaczewski explores the challenges involved in data collection, processing, and management, as well as model development and validation. He also provides an overview of the necessary hardware and software infrastructure. Read more.
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9:459:55 Thursday, 17 October 2019
Location: King's Suite
Walter Riviera (Intel)
Average rating: **...
(2.64, 11 ratings)
Walter Riviera details three key shifts in the AI landscape—incredibly large models with billions of hyperparameters, massive clusters of compute nodes supporting AI, and the exploding volume of data meeting ever-stricter latency requirements—how to navigate them, and when to explore hardware acceleration. Read more.
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11:5512:35 Thursday, 17 October 2019
Location: Westminster Suite
Siddha Ganju (NVIDIA), Meher Kasam (Square)
Average rating: ****.
(4.80, 5 ratings)
Over the last few years, convolutional neural networks (CNNs) have risen in popularity, especially in the area of computer vision. Many mobile applications running on smartphones and wearable devices would benefit from the new opportunities enabled by deep learning techniques. Siddha Ganju and Meher Kasam walk you through optimizing deep neural nets to run efficiently on mobile devices. Read more.
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11:5512:35 Thursday, 17 October 2019
Location: Buckingham Room - Palace Suite
Alasdair Allan (Babilim Light Industries)
Average rating: ****.
(4.50, 4 ratings)
The future of machine learning is on the edge and on small, embedded devices that can run for a year or more on a single coin-cell battery. Alasdair Allan dives deep into how using deep learning can be very energy efficient and allows you to make sense of sensor data in real time. Read more.
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16:5017:30 Thursday, 17 October 2019
Location: King's Suite - Sandringham
Jim Dowling (Logical Clocks), Ajit Mathews (AMD)
Average rating: *****
(5.00, 1 rating)
The Radeon open ecosystem (ROCm) is an open source software foundation for GPU computing on Linux. ROCm supports TensorFlow and PyTorch using MIOpen, a library of highly optimized GPU routines for deep learning. Jim Dowling and Ajit Mathews outline how the open source Hopsworks framework enables the construction of horizontally scalable end-to-end machine learning pipelines on ROCm-enabled GPUs. Read more.

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