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Tiny AI

To grasp all the possibilities of artificial intelligence, you need an approach that integrates innovations in data usage, hardware and software. As an R&D hub that connects all these capabilities, imec is ideally situated to help you out.

Big dreams, big possibilities, ... and big challenges. That’s the state of artificial intelligence today. Colossal data sets are pumped into gigantic cloud data centers, to be analyzed by endless algorithms. It’s a process that:

  • relies on a tremendous amount of energy
  • raises considerable privacy concerns
  • requires huge bandwidths

This model is economically and ecologically unsustainable. Big AI is unsuited for offline and realtime decision-making – such as we need for autonomous driving or connected health solutions. And it’s unable to bring us closer to the dream of autonomous, human-like systems.

In order to dream big, we must think small. Or even tiny.

Download our white paper on the roadmap for edge AI

Integrated approach

The era of cloud dominance is ending: future AI environments will be decentralized. Edge and extreme edge devices will do their own processing. They will send a minimum amount of data to a central hub. And they will work – and learn – together.

That doesn’t mean that cloud centers will become a thing of the past. They will play a key role, for instance in high-performance computing applications such as DNA analysis. But they need to become drastically more efficient to deal with petabytes of data in hours instead of days.

Tiny AI is imec’s answer towards reaching those goals. It’s an integrated approach that involves co-development of data, hardware and algorithms.  

Tiny data

Smarter data usage is the first step towards hyper-efficient AI systems. Imec is looking into solutions such as:

  • data reduction through techniques such as surrogate modeling
  • alternative data sources such as radar sensing systems
  • unsupervised learning methods such as GAN and LSTM
  • compression strategies such as network pruning
  • AI-assisted data processing

Tiny hardware

Through advances in nanotechnology, imec keeps pushing the limits of doing more with less:

Tiny algorithms

In order to enable energy-efficient processing at the (extreme) edge, imec delivers on-chip AI algorithms for almost all its endpoints. We make them smarter by using:

  • new edge-learning methodologies such as distributed and joint learning
  • alternative ANN architectures such as spiking
  • sensor fusion strategies
  • adaptive inference techniques, used for example in the LEAPS project
  • transfer learning approaches

Applications of Tiny AI

Soon, just about every business will be a technology business. Here are some key sectors in which Tiny AI will have a massive impact.

Tiny AI in healthcare

The big dream in healthcare is personalized medicine. It will be driven by our improved ability to gather data and turn it into actionable insights. Some examples:

Tiny AI in Industry 4.0

One day, we will see cobots that autonomously collaborate with humans on the factory floor. And it will be thanks to the innovations in Tiny AI. Already today, the way we make and grow things is being revolutionized by artificial intelligence:

  • A project like DyVerSIFy mines, analyzes and visualizes companies’ sensor data in order to detect errors and anomalies, and to improve the efficacy of maintenance and design.
  • In agriculture, a combination of affordable sensing and smart algorithms helps to improve yields by means of precision farming – e.g. the MoniCow project or automatic crop disease detection through hyperspectral imaging.
  • Real-time and context-aware anomaly detection – explored in the RADIANCE project, for instance – paves the road for automatic decisioning and other efficiency gains.

Tiny AI in mobility and logistics

The world is anxiously waiting for autonomous and connected cars to appear on our roads. In several ways, Tiny AI will play a key role in realizing those possibilities:

Want to use Tiny AI to make smart solutions?

Imec helps you with technical AI challenges such as:

  • extraction of quality data at the (extreme) edge
  • adaptive processing of multi-dimensional signals from distributed sensors
  • analysis and understanding of anomalies in combined data streams
  • automated decisioning in specific environments

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