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Moshe Sheyer Hello

The global semiconductor market is on fire, with the World Semiconductor Trade Statistics (WSTS) predicting robust growth of 16% in 2024 and an additional 12.5% in 2025. One key contributor to this growth is the booming wearables segment. Earwear, smartwatches, fitness bands, and even emerging smart rings are expected to drive wearable shipments well in excess of 500 million units this year. These devices are increasingly packing more technology inside and are now beginning to leverage artificial intelligence (AI) functionality via Tiny Machine Learning (TinyML), a form of AI specifically designed for low-power, resource-constrained devices. TinyML applications in wearable devices are enhancing functionalities such as processing voice commands, analyzing visual and health data, and interpreting vibration sensors, to bring the power of AI on-device. To learn more, check out Ceva’s latest Neural Processing Unit (NPU) IP tailored for TinyML applications in embedded devices. Enjoy reading!


Moshe Sheier, VP Marketing, Ceva
 
Ceva-Neupro-Nano
Articles
Blog: NPU IP Architecture Shaped
Ido Gus,  Deep Learning Senior Team Leader, Sensor and Audio BU, Ceva.
The emergence of TinyML (Tiny Machine Learning) has further pushed the boundaries of AI, focusing on implementing machine learning algorithms on resource-constrained embedded devices. TinyML aims to enable AI capabilities on billions of edge devices, allowing them to process data and make decisions locally and in real-time without relying on cloud connectivity or powerful computing resources. 
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Blog: Creating a repeatable system to evaluate spatial audio
Kaushik Sethunath, Audio Test Engineer and Content Developer at CEVA.
It seems as though there is a lot of confusion surrounding spatial audio, its scope, and how to effectively quantify what a ‘good’ spatial audio experience should be. Accordingly, there seems to be a lack of an industry standard framework to evaluate the spatial audio experience. Here at Ceva, we set out to develop such a framework, to evaluate spatial audio in a systematic and repeatable way, providing a guide for anyone to be able to gauge the efficacy of a spatial audio solution.  
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The Ojo-Yoshida Report - The IoT market is yet to see an “explosive growth" in TinyML. Is that due to inadequate hardware, ever-shifting software or not enough ML skills in the embedded community? TinyML in embedded systems can be implemented many ways, often by leveraging beefed-up MCUs, DSPs, AI accelerators and Neural Processing Units (NPUs). The lingering dilemma is how best to develop embedded systems with machine learning that could fit in the budget of TinyML (Access to the reading materials is based on a subscription ).
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Ceva in the News
All About Circuits 27/6/2024
Tiny But Mighty: Ceva Reveals New NPUs for Tiny Machine Learning Devices
"...Ceva introduced the Ceva-NeuPro-Nano family of self-sufficient neural processing units (NPUs). The NeuPro-Nano is an NPU IP set designed for third-party processor manufacturers to integrate it into their system-on-chips (SoCs) or microcontrollers..." 
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IOT Insider 27/6/2024
Ceva adds NPUs for AIoT devices
"...TinyML is defined as the application of machine learning models on devices that are limited in power and resources, enhancing the capabilities of the Internet of Things (IoT). With a surge in demand for effective and specialised AI solutions within IoT devices, the TinyML market is set to expand significantly..." 
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