Sunday, May 19, 2024

A.I.

Researchers in Portugal develop an image analysis AI platform to boost worldwide research


DL4MicEverywhere empowers life scientists to harness cutting-edge deep learning techniques for biomedical research



INSTITUTO GULBENKIAN DE CIENCIA

First author, Ivan Hidalgo-Cenamor, discussing the platform 

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FIRST AUTHOR, IVAN HIDALGO-CENAMOR, DISCUSSING THE PLATFORM

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CREDIT: CREDITS: INSTITUTO GULBENKIAN DE CIÊNCIA





A team of researchers from the Instituto Gulbenkian de Ciência (IGC) in Portugal, together with Åbo Akademi University in Finland, the AI4Life consortium, and other collaborators, have developed an innovative open-source platform called DL4MicEverywhere published today in the journal Nature Methods*. This platform provides life scientists with easy access to advanced artificial intelligence (AI) for the analysis of microscopy images. Itenables other researchers, regardless of their computational expertise, to easily train and use deep learning models on their own data.

Deep learning, a subfield of AI, has revolutionised the analysis of large and complex microscopy datasets, allowing scientists to automatically identify, track and analyse cells and subcellular structures. However, the lack of computing resources and AI expertise prevents some researchers in life-sciences from taking advantage of these powerful techniques in their own work. DL4MicEverywhere addresses these challenges by providing an intuitive interface for researchers to use deep learning models on any experiment that requires image analysis and in diverse computing infrastructures, from simple laptops to high-performance clusters.

"Our platform establishes a bridge between AI technological advances and biomedical research", said Ivan Hidalgo-Cenamor, first author of the study and researcher at IGC. “With it, regardless of their expertise in AI, researchers gain access to cutting-edge microscopymethods, enabling them to automatically analyse their results and potentially discover new biological insights”.

The DL4MicEverywhere platform builds upon the team's previous work, ZeroCostDL4Mic, to allow the training and use of models across various computational environments. The platform also includes a user-friendly interface and expands the collection of available methodologies that users can apply to common microscopy image analysis tasks.

"DL4MicEverywhere aims to democratise AI for microscopy by promoting community contributions and adhering to FAIR principles for scientific research software - making resources findable, accessible, interoperable and reusable", explained Dr. Estibaliz Gómez-de-Mariscal, co-lead of the study and researcher at IGC. "We hope this platform will empower researchers worldwide to harness these powerful techniques in their work, regardless of their resources or expertise".

The development of DL4MicEverywhere is a great example of the collaborative environment in science. First, it was developed with the purpose of allowing any researcher worldwide to take advantage of the most advanced technologies in microscopy, contributing to accelerate scientific discoveries. Second, it was made possible only through an international collaboration of experts in computer science, image analysis, and microscopy, with key contributions from the AI4Life consortium. The project was co-led by Ricardo Henriques at IGC and Guillaume Jacquemet at Åbo Akademi University.

"This work represents an important milestone in making AI more accessible and reusable for the microscopy community", said Professor Jacquemet. "By enabling researchers to share their models and analysis pipelines easily, we can accelerate discoveries and enhance reproducibility in biomedical research".

"DL4MicEverywhere has the potential to be transformative for the life sciences," added Professor Henriques. "It aligns with our vision in AI4Life to develop sustainable AI solutions that empower researchers and drive innovation in healthcare and beyond".

The DL4MicEverywhere platform is freely available as an open-source resource, reflecting the teams' commitment to open science and reproducibility. The researchers believe that by lowering the barriers to advanced microscopy image analysis, DL4MicEverywhere will enable breakthrough discoveries in fields ranging from basic cell biology to drug discovery and personalised medicine.

 

*Iván Hidalgo-Cenalmor, Joanna W Pylvänäinen, Mariana G Ferreira, Craig T Russell, Ignacio Arganda-Carreras, AI4Life Consortium, Guillaume Jacquemet, Ricardo Henriques, Estibaliz Gómez-de-Mariscal (2024) DL4MicEverywhere: Deep learning for microscopy made flexible, shareable, and reproducible. Nature Methods. DOI: 10.1038/s41592-024-02295-6

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NUS researchers and industry partners demonstrate cutting-edge chip technology for ultra-low power AI connected devices



Dramatic improvements in chip energy efficiency will turbocharge Singapore’s AI and semiconductor industry with new capabilities in always-on AI devices



NATIONAL UNIVERSITY OF SINGAPORE






Researchers from NUS, together with industry partners Soitec and NXP Semiconductors, have demonstrated a new class of silicon systems that promises to enhance the energy efficiency of AI connected devices by leaps and bounds. These technological breakthroughs will significantly advance the capabilities of the semiconductor industry in Singapore and beyond.

This innovation has been demonstrated in fully-depleted silicon-on-insulator (FD-SOI) technology, and can be applied to the design and fabrication of advanced semiconductor components for AI applications. The new chip technology has the potential to extend the battery life of wearables and smart objects by a factor of 10, support intense computational workloads for use in Internet of Things applications, and halve the power consumption associated with wireless communications with the cloud.

The new suite of disruptive chip technologies will be promoted through the FD-SOI & IoT Industry Consortium to accelerate industry adoption by lowering the design barrier to entry in FD-SOI chips. An industry workshop titled “Next-gen energy-efficient FD-SOI systems" was held on 3 May 2024 for participants from the industry and research community to share and discuss the latest developments in FD-SOI technologies, and showcase the new capabilities with state-of-the-art demonstrations.

“IoT devices often operate on a very limited power budget, and hence require extremely low average power to efficiently perform regular tasks such as physical signal monitoring. At the same time, high peak performance is demanded to process occasional signal events with computationally-intensive AI algorithms. Our research uniquely allows us to simultaneously reduce the average power and improve the peak performance,” said Professor Massimo Alioto, who is from the NUS College of Design and Engineering’s Department of Electrical and Computer Engineering and is also the Director of the FD-fAbrICS (FD-SOI Always-on Intelligent & Connected Systems) joint lab where the new suite of technologies was engineered.

“The applications are wide-ranging and include smart cities, smart buildings, Industry 4.0, wearables and smart logistics. The remarkable energy improvements obtained in the FD-fAbrICS program are a game changer in the area of battery-powered AI devices, as they ultimately allow us to move intelligence from conventional cloud to smart miniaturised devices,” said Prof Alioto, who also heads the Green IC group (www.green-ic.org) at the Department of Electrical and Computer Engineering.

Powering AI devices with ultra-energy efficient chips

Research conducted by the NUS FD-fAbrICS joint lab showed that their FD-SOI chip technology can be deployed at scale with enhanced design and system integration productivity for lower cost, faster market reach, and rapid industry adoption.

“This innovation has the potential to accelerate the time to market for key players in Singapore’s semiconductor ecosystem,” said Prof Alioto. “We hope to facilitate the adoption and deployment of our design technologies at scale through the FD-SOI & IoT Industry Consortium. This is a significant contribution to the AI and semiconductor industry in Singapore, as it enables a competitive advantage while reducing the overall development cost of FD-SOI systems.”

The research breakthroughs from the NUS FD-fAbrICS joint lab leverage the combined NUS expertise and capabilities from different domains, such as digital circuits (Prof Massimo Alioto), wireless communications (Assoc Prof Heng Chun Huat), system architectures (Asst Prof Trevor Carlson), and AI models (Prof Li Haizhou). Industry leaders such as Soitec, NXP and Dolphin Design contributed to the research efforts at the joint lab, which is also supported by the Agency for Science, Technology and Research.

The NUS research team is now looking into developing new classes of intelligent and connected silicon systems that could support larger AI model sizes (“large models”) for generative AI applications. The resulting decentralisation of AI computation from cloud to distributed devices will simultaneously preserve privacy, keep latency at a minimum, and avoid wireless data deluge under the simultaneous presence of a plethora of devices.

Accelerating industry adoption of FD-SOI technologies

The industry workshop, which delved into the cutting-edge advancements and applications of FD-SOI technology, aimed to foster an environment of knowledge sharing as well as catalyse collaborations within, and between, the FD-SOI research community and the semiconductor industry in Singapore working on intelligent and connected silicon systems.

Another objective of the workshop was to facilitate rapid FD-SOI adoption and lower the design barrier to entry, by sharing the research outcomes from the FD-fAbrICS joint lab. Speakers from Soitec, GlobalFoundries, NXP, and the NUS FD-fAbrICS research team shared their perspectives on the current development of related technologies – for example, in manufacturing and microchip design – and future disruptive technologies for next-generation ultra-low power AI systems.

FD-SOI & IoT Industry Consortium

The FD-SOI & IoT Consortium was established to extend the impact of the NUS FD-fAbrICS joint lab on the semiconductor ecosystem in Singapore. Soitec and NXP are founding members of the Consortium.

Consortium members will have access to innovative FD-SOI design IP and methodologies, which will help to accelerate their next-generation prototyping and development cycle with highly energy efficient processes, especially in the fast-growing area of AI-connected chips.

The FD-SOI & IoT Consortium will support the near-term needs of industry for rapid technology road mapping and accelerated innovation cycle. At the same time, to assure sustained scalability and differentiation across the Consortium members in the longer term, the technologies developed in synergy with the FD-fAbrICS industry partners will be further expanded by some of the Consortium members.

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