Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Today's demand for real-time data analytics at the edge marks the dawn of a new era in machine learning (ML): edge intelligence. That need for time-sensitive data is, in turn, fueling a massive AI chip market, as companies look to provide ML models at the edge that have less latency and more power efficiency. Conventional edge ML platforms consume a lot of power, limiting the operational efficiency of smart devices, which live on the edge.

Vivoka, a Lorraine-based company and French leader in speech recognition with the Voice Development Kit, announced in early January its participation in the partnership program of NXP Semiconductors, the world's tenth largest supplier of embedded controllers. The addition of NXP's technology to Vivoka's voice recognition artificial intelligence solution will benefit customers of both brands. Vivoka, a French company located in Metz, founded in 2015 by William Simonin, develops a solution that allows any company to add a voice interface to its products, very simply. This solution, called VDK (Voice Development Kit), is suitable for kiosks, robots, mobile applications, headsets… and has allowed it to become the French leader in voice recognition. Vivoka won the coveted Innovation Award in the sustainability and eco-design category at CES 2019.

NXP is hoping to improve its machine learning offerings after making a strategic investment in Au-Zone Technologies. The exclusive arrangement specifically concerns Au-Zone's DeepView ML Tool Suite, which will be used to bolster NXP's eIQ Machine Learning software development environment and lead to the creation of new Edge machine learning products. In that regard, the DeepView Suite comes with a graphical user interface (GUI) and workflows that will make it easier to import datasets, and to train neural network models for Edge devices. DeepView's run-time inference engine will give eIQ developers more insight into system memory usage, data movement, and other performance metrics in real time, which will in turn allow them to optimize their model before deploying it in a System-on-Chip (SoC) solution. "This partnership will accelerate the deployment of embedded Machine Learning features," said Au-Zone CEO Brad Scott.

NXP Semiconductors N.V. announced that it is enhancing its machine learning development environment and product portfolio. Through an investment, NXP has established an exclusive, strategic partnership with Canada-based Au-Zone Technologies to expand NXP's eIQ Machine Learning (ML) software development environment with easy-to-use ML tools and expand its offering of silicon-optimized inference engines for Edge ML. Additionally, NXP announced that it has been working with Arm as the lead technology partner in evolving Arm Ethos-U microNPU (Neural Processing Unit) architecture to support applications processors. NXP will integrate the Ethos-U65 microNPU into its next generation of i.MX applications processors to deliver energy-efficient, cost-effective ML solutions for the fast-growing Industrial and IoT Edge. "NXP's scalable applications processors deliver an efficient product platform and a broad ecosystem for our customers to quickly deliver innovative systems," said Ron Martino, Senior Vice President and General Manager of Edge Processing at NXP Semiconductors.

Just a few years ago, it was assumed that machine learning (ML) -- and even deep learning (DL) -- could only be performed on high-end hardware, with training and inference at the edge executed by gateways, edge servers, or data centers. It was a valid assumption at the time because the trend toward distributing computational resources between the cloud and the edge was in its early stages. But this scenario has changed dramatically thanks to intensive research and development efforts made by industry and academia. The result is that today, processors capable of delivering many trillions of operations per second (TOPS) are not required to perform ML. In an increasing number of cases, the latest microcontrollers, some with embedded ML accelerators, can bring ML to edge devices.

On Monday I'll be giving a keynote at the IEEE Custom Integrated Circuits Conference, which is quite surprising even to me, considering I'm a software engineer who can barely solder! Despite that, I knew exactly what I wanted to talk about when I was offered the invitation. If I have a room full of hardware designers listening to me to twenty minutes, I want them to understand what people building machine learning applications need out of their chips. After thirteen years(!) of blogging, I find writing a post the most natural way of organizing my thoughts, so I hope any attendees don't mind some spoilers on what I'll be asking for. At TinyML last month, I think it was Simon Craske from Arm who said that a few years ago hardware design was getting a little bit boring, since the requirements seemed well understood and it was mostly just an exercise in iterating on existing ideas.

In natural hazard warning systems fast decision making is vital to avoid catastrophes. Decision making at the edge of a wireless sensor network promises fast response times but is limited by the availability of energy, data transfer speed, processing and memory constraints. In this work we present a realization of a wireless sensor network for hazard monitoring which is based on an array of event-triggered seismic sensors with advanced signal processing and characterization capabilities for a novel co-detection technique. On the one hand we leverage an ultra-low power, threshold-triggering circuit paired with on-demand digital signal acquisition capable of extracting relevant information exactly when it matters most and not wasting precious resources when nothing can be observed. On the other hand we use machine-learning-based classification implemented on low-power, off-the-shelf microcontrollers to avoid false positive warnings and to actively identify humans in hazard zones. The sensors' response time and memory requirement is substantially improved by pipelining the inference of a convolutional neural network. In this way, convolutional neural networks that would not run unmodified on a memory constrained device can be executed in real-time and at scale on low-power embedded devices.

Artificial intelligence (AI) and machine learning (ML) seem to be in the headlines more and more each year. But for the most part, embedded developers don't seem to pay too much attention. Sure, AI has some really cool applications. For real-time microcontroller developers, however, AI seems to be a distant tool that belongs in the cloud or on a high-end application processor. As it turns out, there are several reasons why embedded developers need to start paying attention to AI and ML right now.

NXP Semiconductors N.V. (NASDAQ:NXPI) announced a comprehensive, easy-to-use machine learning (ML) environment for building innovative applications with cutting-edge capabilities. Customers can now easily implement ML functionality on NXP's breadth of devices from low-cost microcontrollers (MCUs) to breakthrough crossover i.MX RT processors and high-performance application processors. The ML environment provides turnkey enablement for choosing the optimum execution engine from among Arm Cortex cores to high-performance GPU/DSP (Graphics Processing Unit/Digital Signal Processor) complexes and tools for deploying machine learning models, including neural nets, on those engines. Embedded Artificial Intelligence (AI) is quickly becoming an essential capability for edge processing, gives'smart' devices an ability to become'aware' of its surroundings and make decisions on the input received with little or no human intervention. NXP's ML environment enables fast-growing machine learning use-cases in vision, voice, and anomaly detections.

Not surprisingly, this year's smartphones feature faster processors than those from last year--that happens every year. But what is new this year is the predominance of machine learning features that just about every processor vendor is touting as a way of differentiating their devices. This is true for the phone vendors who design their own chips, the independent or merchant chip vendors who sell processors to phone vendors, and even the IP makers who design the cores that go into the processors themselves. First a little background: all modern application processors include designs (often referred to as intellectual property, or IP) from other companies, notably firms like ARM, Imagination Technologies, MIPS, and Ceva. Such IP can appear in various forms--for example, ARM sells everything from a basic license for its 32-bit and 64-bit architecture, to specific cores for CPUs, graphics, image processing, etc., that chip designers can then use to create processors.