Embedded storage elements on next MCU generation ready for AI on the edge
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.
Please, do not hesitate to contact Jiri Kadlec for more information.
Xilinx Vitis AI facedetect Demo on Trenz Electronic TE0820 4EV SoM with TE0701 06 Carrier Board and Avnet HDMI In/Out FMC Card
Xilinx Vitis AI facedetect and resnet50 Demo on Trenz Electronic TE0802 02 with ZU2CG and 1 GB LPDD4
Xilinx Vitis AI 'facedetect' and 'resnet50' Demo on Trenz Electronic TE0821-01-2cg-4GB SoM + TE0706-3 Carrier
Testing all Samples from Xilinx Vitis AI Library 2.0 on Trenz Electronic board TE0808 SoM + TEBF0808 Carrier
All VART Examples from Xilinx Vitis AI 2.0 for Trenz Electronic board TE0808 SoM + TEBF0808 Carrier
Xilinx Vitis AI 'facedetect' Demo on Trenz Electronic board TE0808 SoM + TEBF0808 Carrier
Likhonina, Raissa. Fast Bayesian Algorithms for FPGA Platforms.
(Prague 2022. Thesis.)
EFECS 2022: a success for StorAIge (UTIA demonstrator at StorAIge booth)
|Title:||Embedded storage elements on next MCU generation ready for AI on the edge|
|Project No.:||ECSEL 101007321
|Duration:||1 July 2021 - 30 June 2024|