Moving Machine Learning To The Tiny Edge

时间： 2023-11-28 来源：SILICON LABS

技术问答

From unlocking mobile phones with facial recognition to setting spam filters to receiving product recommendations, we interact with machine learning (ML) every day. Manufacturers of a variety of products know this and are taking advantage or ML to introduce 1eature-rich, low-power products that stand out in the market.

WHAT ARE ML AND DEEP LEARNING?

A subset of artificial intelligence, MLI includes complex statistical techniques that enable machines to improve at tasks with experience. Deep Learning (DL), a subset of ML, encompasses the algorithms that software uses to train itself and improve at performing tasks -like speech and image recognition - by exposing multilayered neural networks to vast amounts of data.

For DL, engineers create multilayer models with many decision layers. To train a model, engineers present it with many examples of data relevant to a pattern, or patterns of interest. Then, the model finds a statistical structure in the example data, develops rules for recognizing that data, and is able to determine if real-world, In-field data is like the patterns learned from the training data.

Once a trained ML model or algorithm is deployed to run inference in the field, the model classifies the real-world data and gives probabilities that this new data represents a relevant pattern of interest. Thanks to the advances of tinyML, deep neural networks are able to perform meaningful tasks on embedded devices with limited resources.

An ML model is an application-specific, Intelligent, pattern-matching algorithm that has to be administered precisely for application to make the right Inference. A precise ML model can give a company a competitive edge by reducing bandwidth requirements, saving power, and increasing a device's ability to make smarter decisions.

ML AT THE EDGE

Due to the complexity and data burdens ML Involves, widescale ML Implementation on the cloud has raised cost, security, and latency concerns. Given these obstacles, ML Is now more often being conducted at the "edge" meaning data can be stored, processed, and analyzed on the physical device or as near the source as possible. Performing these actions in the local environment instead of sending data to the cloud saves money and protects sensitive information like health care data.

As the demand for wireless products and smaller devices grows, developers are recognizing the benefits of the edge and applying MLs pattern matching at the source, or the tiny edge. Edge-native Al is embedded directly into a microcontroller or microprocessor to offer a smaller footprint and centralized, potentially real-time control for smarter machines that are more secure and reliable. SILICON LABS supports ML In all Series 2 wireless SoCs including newly released BG24 and MG24 products with built-In AI/ML hardware accelerator.

ML continues to expand the smart machine possibilities in industries such as automotive, energy, entertainment food health care, housing, industrial automation, and telecommunications. As the number of ML application ideas grows, so do the tools and technologies that enable ML at the IoT edge, or tinyML. With smaller sizes, devices at the tinyML save energy and reduce costs in addition to enhancing security and lowering latency. This means companies will be better able to interpret data, improve customer satisfaction, and address the ML demand, which has grown drastically since the onset of the COVID-19 pandemic. in March 2022. Fortune Business insights estimated that the ML market will grow from $15.44 billion in 2021 to $209.91 billion by 2029.

BIG FUTURE AT THE TINY EDGE
As Intelligence is pushed down from the cloud closer to the source of the data also known as the endpoint or tiny edge(e.g., a light switch or doorbell itself). data analysis using ML Is becoming more efficient. Reducing latency conserving bandwidth, Increasing privacy, and decreasing costs are all benefits of ML on the tiny edge.

Developers are also seeing the advantages of incorporating ML as a value-added feature to existing embedded applications. With all these benefits, the number of devices running ML at the tiny edge is expected to grow from 4 billion to 12 billion over the next four years-what an exciting prospect!

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本文由PlusLee转载自SILICON LABS，原文标题为:Moving Machine Learning To The Tiny Edge，本站所有转载文章系出于传递更多信息之目的，且明确注明来源，不希望被转载的媒体或个人可与我们联系，我们将立即进行删除处理。

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QFP64,SI7006-A20-IM,C8051F502-AQ,SI2165-D,C8051F544-IQ,C8051F986-GU,C8051F34A-GM,SI21832,WF121,EZR32WG230F64R67G,SI8620BT-IS,C8051F569-AM,SI8642BC-B-IS1,C8051F930-GQ,C8051F986-GM,EFM8UB20F32G-A-QFP48,EZR32LG330F64R55G,EFM32LG380FX - QFP100,SI8630

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