Tech distributor Avnet will showcase new innovative technology, applications and solutions in artificial intelligence and machine learning at the Avnet AI Cloud Exhibition, together with its suppliers and partners.

The company will also hold the Avnet 2021 Artificial Intelligence Cloud Conference on 29 June, 2021. Joined by developers, engineers, and decision makers in the AI field, the summit will feature cutting-edge technology trends in artificial intelligence and machine learning, and in-depth discussions on the development, future prospects and blueprints for AI to encourage and accelerate innovation.

“MarketsandMarkets forecasts the global artificial intelligence market size to grow to over USD$300 billion by 2026, and the market in Asia Pacific is anticipated to grow at the highest CAGR during the forecast period,” says KS Lim, senior director of supplier management at Avnet Asia.

“As the world’s leading technology distributor and solution provider, Avnet has a comprehensive ecosystem that provides customers with end-to-end artificial intelligence and machine learning solutions, reducing the cost and complexity of product development to enable application scenarios,” he says.

“We will continue to work hand in hand with our suppliers and partners to further contribute to the development and maturity of the entire AI ecosystem.”

The virtual exhibition is divided into three sections: AI smart solution demonstration area, Avnet design service demonstration area, and a partner solution demonstration area. 

In the AI smart solution demonstration area, participants can learn about Avnet’s various innovative technologies and industrial applications, including:

AI camera: A smart AI camera utilising a neural network implemented in the FPGA fabric. It integrates an independent high-performance ISP camera module based on the Xilinx Zynq7020 to achieve a variety of functions, including noise reduction, wide dynamic range, light source detection, motion detection and edge enhancement function.

BlueBox AI platform: The embedded edge artificial intelligence box can perform multi-channel convolutional neural network operations. It facilitates real-time multi-channel AI functions such as face detection, passenger and traffic statistics, and license plate recognition. All functions operate independently and can work simultaneously to provide edge artificial intelligence analytic solutions. 

The box integrates all the above functions through the underlying Xilinx Zynq UltraScale+ MPSoC to perform AI computing on demand.

ROS on Ultra96: The open source Robot Operating System (ROS) runs on the Avnet Ultra96 development board, which features the Xilinx Zynq UltraScale+ MPSoC. The programmable logic part of the Zynq UltraScale+ MPSoC provides deep learning acceleration capabilities, while consolidating a range of ROS functions such as control, SLAM, and navigation. The small form-factor Ultra96 single board computer running ROS makes an ideal platform for developing autonomous robots, service robots, and general purpose ROS experimentation.

In the partner demonstration area, Avnet’s suppliers and partners will also showcase their innovations:

ON Semiconductor: A variety of advanced imaging technologies such as high speed, short exposure, global shutter and platform solutions will be displayed to address the application of different scenarios such as factory automation and the challenges faced in industrial AI applications, to accelerate innovation.

Samtec: Fast-growing technologies like Artificial Intelligence are driving new system architectures that demand increased bandwidths, frequencies and densities. To meet these challenges, Samtec offers innovative high-performance interconnects that exceed AI industry standards.

STMicroelectronics: Introduce embedded AI solutions based on deep learning models running on high performance 32-bit microcontroller and also machine learning based MEMS sensor.Western Digital: IX SN530 NVMeTM industrial-grade SSD will be displayed, which supports a new generation of data-rich industrial design and autonomous vehicle design.

Xilinx: Will showcase its real-time multi-task autonomous driving AI perception processing solution, which uses the industry-leading lightweight optimisation algorithm to achieve vehicle detection, lane line detection, lane detection in ADAS and autonomous driving scenarios through a single model. It can perform multiple tasks such as driving area detection and depth estimation.

In addition, Xilinx will also demonstrate the application of Versal-based DPU in low-latency automatic driving and pose detection.

YAGEO Group: Will showcase the flagship products from its main brands YAGEO, KEMET and PULSE, including YAGEO resistors, KEMET polymer capacitors, and PULSE network devices, to provide high reliability polymer and ceramic capacitor solutions for AI chips and DC power supplies for autopilot computers.

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Last Thursday, a range of experts joined the Advanced Scale Forum (ASF) in a rapid-fire roundtable to discuss how advanced technologies have transformed the way humanity responded to the COVID-19 pandemic in indelible ways. The roundtable, held near the one-year mark of the first lockdowns in North America, opened with a session from Ari Berman, CEO of BioTeam.

“It’s so easy to focus on the bad things we hear about the remarkable and really unfortunate numbers of people who have died from this, the huge numbers of people who’ve been infected from it, we talk about these new more infectious variants, et cetera,” Berman said – but, he added, there were major success stories in the pandemic, too: collaborations and technology deployments that will save “millions of lives.”

NIH Keynote: Creating a Coordinated Data Approach to Help Address COVID-19

With that, the roundtable launched into its first keynote, delivered by the National Institutes of Health’s Susan Gregurick, who serves as associate director for data science and director of the NIH’s Office of Data Science Strategy. 

“We’ve been working for almost a year now to sprint ahead to collect and enhance SARS-CoV-2 data – clinical data, structural data, genomics data – to address the pandemic,” Gregurick said. “The first thing that we tried to do – and we did successfully – was to get different types of at-home, point-of-care clinical testing technologies out into the hands of our citizens.”

This program – called RADx – ranges from preparing for high-throughput COVID-19 testing to engaging underserved populations through community-engaged implementation projects, and it’s one of several data-driven projects run inside the NIH. The NIH has also, for instance, been working on its Collaboration to Assess Risk and Identify Long-Term Outcomes (CARING) for Children with COVID Program. 

Still, the NIH needed to develop a longer reach. They worked with the National COVID Cohort Collaborative (N3C), which integrates electronic healthcare record data on COVID-19, augmenting it with “an incredibly rich set of data from vulnerable populations.” As of a few months ago, the N3C has multiple millions of participants contributing data to hundreds of ongoing projects and collaborators. (The data is accessible in a cloud archive, which is accessible here.) 

The NIH also worked with the All of Us Research program – which collects longitudinal COVID-19 health outcome data alongside phenotypic and serological data – the BioData Catalyst, which provides data from clinical trials and observational studies such as those that evaluated hydroxychloroquine early in the pandemic. 

Soon enough, the NIH found itself serving as an aggregator of a wide range of data from various sources – and having to grapple with the logistical implications of coordinating both the data and access to the data across a wide range of interested parties.

“Making all this work together across many different projects really does require some efforts in data harmonization,” Gregurick said. “We’ve been tackling this in two different ways: … common data elements and mapping to data models. In some cases it’s a development of curation strategies within the data hub, … in other cases it’s at the point of collection and really collaborating with our data coordination centers.” 

The different stages of the RADx program, for instance, shared around 16 common data elements (CDEs) that could be more easily integrated, but each program also contained its own unique elements. “We’re using those common data elements to help construct data models and data search strategies for ontology,” Gregurick said. “We’re also mapping these to a common data model.” 

The NIH has also been working on unifying other supporting technology, such as the researcher authentication services that allow access to various data, tools and hubs, across platforms. More ambitiously, they’re piloting a program to allow the linking of records from a given individual across platforms without compromising that individual’s identity.

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