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Object Classification Techniques using the OpenMV H7 Camera

Presented by Lorenzo Rizzello

Machine Learning for embedded systems has recently started to make sense: on-device inference reduces latency, costs and minimizes power consumption compared to cloud-based solutions. Thanks to Google TFLite Micro, and its optimized ARM CMSIS NN kernel, on-device inference now also means microcontrollers such as ARM Cortex-M processors.

In this session, we will examine machine vision examples running on the small and power-efficient OpenMV H7 camera. Attendees will learn what it takes to train models with popular desktop Machine Learning frameworks and deploy them to a microcontroller. We will take a hands-on approach, using the OpenMV camera to run the inference and detect objects placed in front of the camera.

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Low-Power Algorithmic Approaches in DSP Implementations

Presented by Bryant Sorensen

Hearing aid signal processing is a challenging task because of the extreme low-power, highly-constrained cycle performance required. The audio signal processing is always on, and requires complex algorithms and computations. A typical hearing aid will have multi-band analysis and synthesis, automatic feedback cancellation, environment detection and action, automatic gain control, and user interface - and AI is arriving as well. In order to reconcile the two disparate requirements (complexity vs. low power & reduced cycles), various approaches are needed to achieve low power while still providing sophisticated calculations. In this talk, I will discuss a sampling of numerical methods, shortcuts, refactorings, and approximations which significantly lower power in DSP algorithms. This will be an overview which I hope sparks thinking to extend the presented concepts to other low-power algorithmic tasks. While the focus is on algorithms and computations, some of these topics will also touch on implications to HW design, HW vs. FW tradeoffs, and ASIP / programmable DSP core design.

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Unifying DSP, adaptive signal processing, algebra and AI

Presented by Amir Kapetanovic

Unifying DSP, adaptive signal processing, algebra and AI? The talk will roll out multiple pathways of dealing with issues with the look back at the last 30 years where we started, made progress, arrived to dilemmas, the cross roads, create alternative directions, some of which died, some of which survived, an many which remain untouched and not elaborated. A portrait of the past and portrait of the possible future. There have been many attempts to define the mainstream. The mainstream definition is not so simple - it does not depend of the level of the abstraction but of the level of feasibility measured by many cost penalties often invisible to many. The talk would expose these pathways in the technology provider agnostic way.

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