Since the breakthrough application of Deep Neural Networks algorithms (DNNs) to speech and image recognition, the number of applications that use DNNs has exploded, achieving the highest accuracy in a myriad of contexts (health, robotics, finance, gaming, etc.). However, their superior accuracy comes at the cost of high computational complexity. Current approaches to solve this challenge are cloud-based, incurring in high power consumption and high latency, given their communication needs. Although cloud approaches are suitable for some context, they are suboptimal for real-time applications running on embedded or mobile devices (with limited battery capacity and requiring fast responses). REEXEN appears to bring a solution to this challenge: an extremely efficient AI processor (a semiconductor chip) specifically designed for supporting DNN-based edge applications. By exploiting state-of-the-art semiconductor technologies in mixed-signal circuits and in-memory processing, REEXEN obtains the best power-effic

IMOCO4.E targets to provide vertically distributed edge-to-cloud intelligence for machines, robots and other human-in-the-loop cyber-physical systems having actively controlled moving elements. They face ever-growing requirements on long-term energy efficiency, size, motion speed, precision, adaptability, self-diagnostic, secure connectivity or new human-cognitive features. IMOCO4.E strives to perceive and understand complex machines and robots. The two main pillars of the project are digital twins and AI principles (machine/deep learning). These pillars build on the I-MECH reference framework and methodology, by adding new tools to layer 3 that delivers an intelligible view on the system, from the initial design throughout its entire life cycle. For effective employment, completely new demands are created on the Edge layers (Layer 1) of the motion control systems (including variable speed drives and smart sensors) which cannot be routinely handled via available commercial products. Based on this, the subsequent mission is to bring adequate edge intelligence into the Instrumentation and Control Layers, to analyse and process machine data at the appropriate levels of the feedback control loops and to synchronise the digital twins with either simulated or real-time physical world. At all levels, AI techniques are employable. Summing up, IMOCO4.E strives to deliver a reference platform consisting of AI and digital twin toolchains and a set of mating building blocks for resilient manufacturing applications. The optimal energy efficient performance and easy (re)configurability, traceability and cyber-security are crucial. The IMOCO4.E reference platform benefits will be directly verified in applications for semicon, packaging, industrial robotics and healthcare. Additionally, the project demonstrates the results in other generic “motion-control-centred” domains. The project outputs will affect the entire value chain of the production automation and application markets.

The main objective of the storAIge project is the development and industrialization of FDSOI 28nm and next generation embedded Phase Change Memory (ePCM) world-class semiconductor technologies, allowing the prototyping of high performance, Ultra low power and secured & safety System on Chip (SoC) solutions enabling competitive Artificial Intelligence (AI) for 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. The development of the most advanced automotive microcontrollers in FDSOI 28nm ePCM will be the support technology to demonstrate the high performances path as well as the robustness of the ePCM solution. The next generation of FDSOI ePCM will be main path for general purpose advanced microcontrollers usable for large volume Edge AI application in industrial and consumer markets with the best compromise on three requirements: performances, low power and adequate security. On top of the development and industrialization of silicon process lines and SoC design, storAIge will also address new design methodologies and tools to facilitate the exploitation of these advanced technology nodes, particularly for high performance microcontrollers having AI capabilities. Activities will be performed to setup robust and adequate Security and Safety level in the final applications, defining and implementing the good ‘mixture’ and tradeoff between HW and SW solutions to speed up adoption for large volume applications.