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StorAIge: Embedded storage elements on next MCU generation ready for AI on the edge

StorAIge is a project funded by the EU/ECSEL and Vinnova - aiming to develop dependable, low-energy and efficient (SW and HW) solutions for AI at the edge. StorAIge involves some 50 European partners, led by ST Microelectronics. The Swedish subconsortium in StorAIge is led by KTH and includes Atlas Copco Industrial Technique, Strikersoft and Uppsala University. The 3-years project runs between 1st of July 2021 until the 30th of June 2024.

AI – and specifically machine learning and deep learning, is now being used in an ever-growing range of applications, considered as a key enabling technology and as a potential major business disrupter. AI is linked to new and/or ‘smarter’ functions and usages, where new technology developments are driven by the application requirements.

The overall aim of the StorAIge project is to setup a world-class manufacturing platform for silicon with Artificial Intelligence capabilities, prototyping high performance, low power, dependable and secure semiconductor technologies competitive AI at the edge applications.

The main challenge addressed by the project is on one hand to handle the complexity of sub-28nm ‘more than moore’ technologies and to bring them up at a high maturity level and on the other hand to handle the design of complex SoCs for more intelligent, secure, flexible, low power consumption and cost effective. The project is targeting chipset and solutions with very efficient memories and high computing power targeting 10 Tops per Watt. Moving AI from the cloud to the edge will orient the StorAIge technological developments towards 3 interdependent challenges: 

* Increase the computing power (high performance)
* Lower the energy consumption (energy-efficient)
* Implement adequate security & privacy level.

The goals for the Swedish subconsortium ( /) include:

  1. Use edge AI to enhance functionality and improve the value of two product families (and use cases), one each from Atlas Copco and Strikersoft.

  2. Build competence in edge AI algorithms and dependable system designs. 

  3. Empower non-specialists to design ASIC comparable efficient hardware. 

The Atlas Copco use case is in the manufacturing domain for assembly augmented with additional sensors and analytics. The use case of Strikersoft concerns an extension of their patient record monitoring product, to detect anomalies to support the health care staff.  

The two universities (KTH and Uppsala) will contribute with the algorithms, implementations and validation in the form of physical and virtual prototypes.  

Contact persons at KTH:

  • Swedish subconsortium managerial lead: Prof. Martin Törngren (
  • ​​Swedish subconsortium technical lead: Prof. Ahmed Hemani ( 

StorAIge has received funding from the ECSEL Joint Undertaking (JU) under Grant Agreement N°101007321. The JU receives support from the European Union’s Horizon 2020 research and innovation programme in France, Belgium, Czech Republic, Germany, Italy, Sweden, Switzerland, Turkey. 

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