Linley Spring Processor Conference 2020

Held April 6-9, 2020
Proceedings available

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Agenda for Day One: Monday April 6, 2020
View Day Two

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9:00am-9:40amKeynote Presentation

The Next Generation of AI Processors
Linley Gwennap, Principal Analyst, TechInsights

In the data center, new architectures are emerging to challenge the GPU's dominance in AI training and inference. Embedded systems such as smart cameras, smart vehicles, and smart robots require powerful accelerators. AI accelerators are even moving into IoT and smart-home devices, running simple neural networks on milliwatts of power. This presentation will describe the latest trends in AI acceleration while addressing how these accelerators are used across this range of end applications.

Q&A immediately following this keynote.

9:40am-9:50amQuestion and Answer
9:50am-10:00amBreak Sponsored by Arm
10:00am-12:15pmAI for Ultra-Low-Power Applications

Microcontroller-based systems can run neural networks to analyze sensor data or listen for "wake words" or other unusual audio events. They can even perform simple visual tasks such as people counting. These networks can run on the CPU, but even a tiny accelerator can provide significant power savings. This session, led by The Linley Group senior analyst Mike Demler, addresses several different approaches for what some peopIe call TinyML.

Introducing the ADK1000 IP and NSoC for Edge AI IoT
Anil Mankar, Cofounder, Chief Development Officer, BrainChip

This presentation will introduce product details of the ADK1000, BrainChip's first event domain neural-network IP and SoC (NSoC) device, which enables AI capability in edge IoT systems at ultra-low power consumption. Based upon the fundamentals of neuromorphic computing, the neural processor can run a standard CNN by converting it into the event-domain, allowing it to implement transfer learning and incremental learning on the chip. The same neural processor can also train and run SNNs natively.

10:20am-10:30amQuestion and Answer
10:30am-10:50amGrAI Matter Labs

Why Waste 95% Power at the Edge?
Jonathan Tapson, Chief Scientific Officer, GrAI Matter Labs

Edge machine learning generally involves real-time video or audio feeds that exhibit great sparsity – sparsity in time, as information rarely changes; sparsity in space, as large sections of visual or audio space contain no information; and sparsity in processing, as edge neural networks can have a majority of inactive connections in any processing period. This presentation will show how GrAI Matter Labs' architecture can exploit all three types of sparsity to achieve new levels of efficiency in edge processing.

10:50am-11:00amQuestion and Answer

New Endpoint AI Technologies: the Arm Cortex-M55 processor and Ethos-U55 microNPU
Tim Menasveta, Senior Product Manager, Arm

The intersection of IoT, AI and 5G is driving the need for more on-device intelligence in smaller, cost-sensitive devices. Arm recently announced new ML IP for microcontrollers: the Cortex-M55 processor, the first to feature Helium technology, and the Arm Ethos-U55 microNPU (neural processing unit), the industry's first microNPU designed to accelerate ML performance. This presentation will describe the processors' features, configuration options, system design considerations, and how they can be used to address a wide range of use cases.

11:30am-11:45amQuestion and Answer
11:45am-12:45pmBreakout sessions with today's speakers


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