AI research

Generative AI based on large language models (LLMs) has taken the world by storm. We’ve only begun to explore its seemingly endless possibilities. Yet researchers at the forefront of innovation are also looking at other approaches to drive artificial intelligence forward.

For AI to reach a level that comes close to artificial general intelligence (AGI), it must take giant leaps forward. LLMs cannot be the only answer. And sustainability, in particular energy consumption, should be a prime concern.

Energy consumption is also a prime concern when it comes to Edge AI. In this crucial domain for a smart, connected environment, specific constraints in terms power budget, but also size and (wireless) communication, require different choices in terms of algorithms, architectures and technologies.

Imec’s AI research combines deep knowledge of (beyond) CMOS semiconductor technologies with expertise on algorithms and architectures to develop the integrated technology blocks that will drive tomorrow’s AI solutions.

Our guiding principle is that these solutions will need to be co-optimized, modular, and application-driven.

AI’s sustainability problem is situated on three levels:

1. technological – There’s a risk computational power will not keep up with application demands, causing a new AI winter.
2. economic – AI relying on continuously expanding data centers is only attainable to the biggest players on the market, an unfair situation likely to stifle innovation.
3. environmental – The energy consumption of these data centers threatens to expand AI’s footprint beyond what our planet can support.
    

 To address this issue, imec’s AI research targets three layers of the technology stack:

1. Algorithms

Imec develops novel algorithms for purposes such as neural network training and sensor processing. This often fits into specific applications such as health devices or sensor fusion use cases like autonomous driving and smart manufacturing.

Especially when it comes to AI at the (extreme) edge, efficient algorithms are a way to reduce energy consumption. But software strategies can also help high-performance computing (hpc) applications, to get more out of available hardware resources. This is the domain of imec’s hpc software (or ExaScience) lab, which combines software AI knowhow with an in-depth understanding of the next level of the stack: architectures.

2. Architectures

On the architectural level, imec’s research teams focus on subjects such as:

• AI accelerators based on compute-in-memory architectures
• neuromorphic/neuro-inspired architectures, using techniques like spiking event-based processing and spiking neural networks (SNN)
• processor architectures
   

In close conjunction with all these activities, imec’s compute system architecture (CSA) team uses its expertise in system-level modeling, performance analysis and hardware validation to explore optimized architectures for scalable systems.

3. Technology

The semiconductor technology layer is the core of imec’s decade-long expertise. Research activities range from traditional CMOS scaling to CMOS 2.0, integrated photonics, and emerging technologies such as quantum computing and superconducting digital computing.

Do we want these innovations to have maximum effect on system performance? Then it’s crucial they’re developed closely together, with that system performance as the north star.

Co-optimization of technology, architectures and algorithms

Since 2010, the computational complexity of AI models increases hundredfold every two years. By comparison, Moore’s law, underpinning the increase in computational power, decrees a ‘mere’ doubling of the number of transistors on a chip every two years. Evidently, the current growth of AI risks being unsustainable.

Modular approach

This co-optimization of algorithms, architectures and technology will result in different functional units with an optimal configuration of technologies to address certain tasks. These ‘AI bricks’ will target specific AI workloads, such as perception, language processing, language generation, etc.

Together, these AI bricks constitute the modular configurations that can handle the heterogeneous AI workloads that, according to experts, will characterize future AI systems.

Application-driven research strategy

This all implies that to design – not necessarily build – these future AI systems requires a relatively detailed conception of the tasks they will need to fulfill. Therefore, application characteristics need to be taken into account from day one.

Imec’s deep involvement in a wide array of application domains allows access to unique insights into these domains’ specific needs and challenges. It enables us to translate knowledge on sensors and algorithms to workloads at the hardware architecture and technology level.

Some crucial applications that imec’s AI teams currently focus on:

• ADAS for automotive through SENSAI, a digital twin for next-gen automotive sensors
• AI for health applications such as biomanufacturing, neuroscience and proteomics
• digital twins for improved process control in semiconductor manufacturing

Additionally, imec is involved in various large-scale testbeds using current technologies, such as Mobilidata, Solid, and various projects within the OnePlanet Research Center.

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