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Join our series of AI Virtual Tech Talks on developing the latest AI technologies. AI Virtual Tech Talks are hosted by experts from Arm and speakers from partner companies in the AI Partner Program. Each session features a different speaker discussing the latest trends in AI development, including deployment, optimization, best practices, and new technologies shaping the industry.
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| Date |
Title |
Speakers |
| February 23, 2021 | Hands-on with PyArmNN for object detection | Arm |
| March 9, 2021 | Automate tinyML Development & Deployment with Qeexo AutoML | Qeexo |
Date: February 23, 2021
Time: 8am PST / 4pm BST / 11pm CST
Karl Fezer, Arm
Join the Arm AI Evangelism team for a live hands-on session with object detection: no setup needed, just a browser. Learn how to run object detection through PyArmNNs API. Just for notification, this workshop is limited to 50 attendees.
Date: March 9, 2021
Time: 8am PST / 4pm GMT / 11pm CST
Tina Shyuan, Director of Product Marketing at Qeexo
What’s the smallest machine learning model you’ve built? Qeexo’s is so small, it can even run on an Arm Cortex M0+! Experience how easy it is to automate "tinyML" machine learning development for sensor modules – without having to write a single line of code!
In this workshop-style Tech Talk, we will:
Prizes to be won!
As a bonus for attending, you could win a very exciting prize! Our hardware sponsor, ST Microelectronics, is giving away 50+ units of the ST SensorTile.box sensor module with Arm Cortex-M4 MCU to select attendees which you can use to experience the power of Qeexo AutoML firsthand. If you are amongst the first to email Qeexo with the secret word from the Tech Talk you will be eligble to win so make sure you attend!
We look forward to seeing you there.
Speaker: Stuart Feffer, co-founder and CEO, Reality AI
Reality AI is one of the leading product development environments for TinyML on Arm MCUs. Tools that generate TinyML models without code have become commonplace, but there is so much more you can do.
We’ll show you how to use AI to drive sensor selection and placement and determine minimum component specifications. We will also look at how to minimize the cost of data collection, and also how to generate sophisticated, explainable models based on sensor data for Arm architectures - automatically. We will use case studies to explore the difference between doing projects and building products, showing examples from Reality AI Tools 4.0.
Speakers: Owen Nicholson & Paul Brasnett, SLAMCore
Spatial AI is the term we use to describe the technologies necessary to give an autonomous robot or drone full awareness of its pose combined with the geometry and semantic understanding of the world around it. At the core of spatial AI is VI-SLAM (visual-inertial simultaneous localisation and mapping), which fuses image and IMU sensor data to understand the world. The algorithms to perform VI-SLAM are computationally demanding and are challenging to implement on embedded processors such as Arm A-class processors.
In this talk we will:
Speakers: Philip Bockrath, Clever Devices; Markus Levy, NXP Semiconductor; David Steele, Arcturus
Smart cities move millions of people every day and they do it by relying on Intelligent Transportation Systems (ITS). This AI Tech Talk brings together a powerhouse team including NXP, Arcturus and Clever Devices, North America’s largest ITS solution provider. The session will focus on edge and AI in public transportation including real-world use cases in public safety and operations. Learn first-hand from the experts about how solutions are implemented and catch a glimpse of where edge AI is heading. The talk will also offer a sneak peek into Arm’s latest offering focused on powering this new wave of edge AI. Join us during this Tech Talk for a rare combination of implementation insights and industry vision.
Speaker: Davis Sawyer, Deeplite
Deep Neural Networks (DNNs) are the de facto tool for solving complex problems, from DNA sequencing to facial recognition. However, DNNs require extensive computing power, memory and expertise to deploy in real world environments. We will discuss various approaches to DNN model optimization for multiple objectives like inference latency, accuracy, and model size to make deep learning more applicable for ARM microcontrollers and low-power devices. Please come with your questions and we'll have an open mic session to let you engage with the speaker.
Speakers: Naveen Suda, Arm; Lingchuan Meng, Arm
Neural Networks have seen tremendous success in interpreting the signals from our physical world, such as vision, voice, and vibration. However, the unprecedented intelligence brought by the neural networks only becomes significant when we deploy them on ubiquitous mobile and IoT devices. Resource constraints of hardware and diverse ML features call for a new and critical component on top of the traditional end-to-end development process: model optimization and deployment. We envision that efficient ML inference lies in the co-development between models, algorithms, software, hardware. Model optimizations serve as an introduction to this process. The first in a planned series, this talk from our Arm engineers provides an overview of model optimization and deployment.
Speaker: Dr. Nitin Gupta, Dori AI
Every enterprise has blind spots that impact their business because of a lack of real-time visibility into people, assets, and workflows. The majority of the tasks still go unmonitored and thus improving productivity is a high priority for all enterprises. AI and computer vision have enabled many enterprises to gain actionable insights through a mechanism called visual process automation (VPA). Enterprises are looking to embedded computer vision to provide the visual intelligence required for their IoT applications. The challenge with most VPA solutions is that they are inherently biased.
Recent analyses of public domain ML models have revealed that many of them are inherently biased. This bias originates from the image and video datasets that were used to originally train the model. The lack of access to volumes of quality data is the reason why many enterprises are seeing biased results from their computer vision models. Enterprises are looking for solutions to enable their ML solutions to be more accurate, robust, and unbiased.
In this talk, we will introduce a structured methodology to address the issue of data and model bias that may be inherent in the ML models you are building. We will provide insights into techniques that help to answer the following questions:
Speaker: James Conroy, Arm
Increasingly, devices are performing AI at the furthest point in the system – on the edge or endpoint devices. As the industry-leading foundation for intelligent computing, Arm’s ML technologies give developers the comprehensive hardware IP, software, and ecosystem platform.
Arm NN is an accelerated inference engine for Arm CPUs, GPUs, and NPUs. It executes ML algorithms on-device to make predictions based on input data. Arm NN enables efficient translation of existing neural network frameworks, such as TensorFlow Lite, TensorFlow, ONNX, and Caffe, allowing them to run efficiently and without modification across Arm Cortex-A CPUs, Mali GPUs, and Ethos-N NPUs.
In this technical session, we are going to give you an overview of Arm NN with a focus on its plug-in framework. The audience will walk away with a working knowledge of using Arm NN to run ML models with accelerated performance on a mobile phone or a Raspberry Pi, and writing plug-ins to extend support for new neural network processing units.
Speakers: Jan Jongboom, CTO , Edge Impulse
In this talk, Jan Jongboom talks about techniques for enabling efficient and explainable TinyML. Learn about efficient optimization from dataset to deployment across the whole spectrum of Arm hardware from Cortex-M at 100 uW to Cortex-A mobile and edge devices with Web Assembly. We’ll include a live tutorial, demonstrating how to easily get started with sensor data collection and TinyML algorithm deployment on your smartphone.
Speakers: Pareena Verma and Ronan Synnott, Arm
Advances in processing power and machine learning algorithms enable us to run machine learning models on tiny far edge devices. Arm’s SIMD, DSP extension and vector processing technologies processor bring an unprecedented uplift in energy-efficient machine learning and signal processing performance for next-generation voice, vision or vibration use cases.
Arm also provides a unified software toolchain for a frictionless and fast developer experience. Join this webinar to learn how to get started today to port optimized ML code for the Arm microcontrollers. In this session, we will cover:
Speakers: Vikrant Tomar and Sam Myer, Fluent.ai
In this tech talk, we will cover how Fluent.ai go from training models in high level libraries such as Pytorch and then run the models on Arm Cortex-M4 and Cortex-M7 boards. We will talk about optimizations achieved using the Arm CMSIS-NN library, as well as, neural network optimizations that we use extensively such as quantization, unique model architectures, etc.
Speaker: Francois de Rochebouet, Cartesiam.ai
Cartesiam will demystify artificial intelligence and all the complexity that comes with it, to the embedded developers community. Even without any prior AI skills, developers can use Cartesiam’s NanoEdge AI Studio to quickly and easily integrate ML algorithms with a broad range of applications on Arm-based low-power microcontrollers. AI Studio also enables on-device learning of a nominal behavior and is capable of detecting any variation of this behavior, even in a complex and “noisy” environment. In this session, we will demonstrate how to generate a custom learning library and use it to perform anomaly detection on an STmicroelectronics development board.
Speakers: Fredrik Knutsson, Arm; Felix Johnny, Arm
Deep Neural Networks are becoming increasingly popular in always-on endpoint devices. Developers can perform data analytics right at the source, with reduced latency as well as energy consumption. We will introduce how Tensorflow Lite for Microcontrollers (TFLu) and its integration with CMSIS-NN maximize the performance of machine learning applications. Developers can now run larger, more complex neural networks on Arm MCUs and micro NPUs while reducing memory footprint and inference time.
Speakers: Anthony Huereca, Systems Engineer, Edge Processing, NXP; Kobus Marneweck, Senior Product Manager, Arm
Machine learning inference is impacting a wide range of markets and devices, especially low power microcontrollers and power-constrained devices for IoT applications. These devices can often only consume milliwatts of power, and therefore not achieve the traditional power requirements of cloud-based approaches. By performing inference on-device, ML can be enabled on these IoT endpoints delivering greater responsiveness, security and privacy while reducing network energy consumption, latency and bandwidth usage.
This talk between Arm and NXP's MCU product managers and engineers will explain how developers can efficiently implement and accelerate ML on extremely low-power, low-area Cortex-M based devices with open-source software libraries and tools. The discussion will include a demo on the i.MX RT1060 crossover MCU to show how to create and deploy ML applications at the edge.
Speakers: Ronan Naughton, Senior Product Manager, Arm
The use of machine learning in endpoint applications is becoming increasingly prevalent across a wide range of industries. As model complexity increases, achieving the best results on the underlying Arm architectures is a fundamental necessity for application developers.
The latest release of Arm NN and Arm Compute Library – pivotal parts of the Arm software stack, includes key new features designed to accelerate our industry-leading performance and substantially reduce your development time:
Join Arm engineers to learn how to best leverage these new features for your ML projects, including demonstrations to help you get the best ML performance, no matter what Arm device you support. This session will be followed on Feb 23rd with a deep-dive workshop where you can get hands-on with this new release. Be sure not to miss either of these opportunities to hear from and engage with Arm engineers.
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