Intended for future applications in mobile devices that need complex machine-learning algorithms, it is being described as a one-of-a-kind collaboration effort to enable applications that now need cloud-based server racks, to be executed within battery-powered mobile devices such as cars and smartphones (at the edge of the internet-of-things).
At a time when edge artificial intelligence and machine-learning algorithms are entering day-to-day products and applications such as smart home assistants with natural-language processing, face-recognition-based security systems or autonomous vehicles, the demand for increasingly complex computational algorithms is set to grow further. At the moment, high-end server parks process the data in the cloud. However, sending data to the cloud costs energy, latency, and is often not preferred for privacy reasons. As such, the ultimate edge artificial intelligence applications require intelligent energy-efficient local processing.
TEMPO aims to tackle this challenge by leveraging the process technology platforms that are being developed by European research technology organisations and cooperating foundries in the project, and combining themwith the application and hardware knowledge from further partners. The TEMPO project will evaluate the current solutions at device, architecture and application level, and build a technology roadmap for European AI hardware platforms. The project will leverage MRAM (imec), FeRAM (Fraunhofer) and RRAM (CEA-Leti) memory to implement both spiking neural network (SNN) and deep neural network (DNN) accelerators for 8 different use cases, ranging from consumer to automotive and medical applications.
Commenting Emmanuel Sabonnadiere, CEO at CEA-Leti said: “It is our aim to sweep technology options, covering emerging memories, and attempt to pair them with contemporary (DNN) and exploratory (SNN) neuromorphic computing paradigms. The process- and design-compatibility of each technology option will be assessed with respect to established integration practices and meet our industrial partner roadmaps and needs to prepare the future market of Edge IA where Europe is well positioned with multiple disruptive technologies.”
Prof. Hubert Lakner, Director of the Fraunhofer Institute for Photonic Microsystems (IPMS) and Chairman of the Board of Directors of the Fraunhofer Group Microelectronics: “A key enabler for machine learning and pattern recognition is the capability of the algorithms to browse through large datasets. Which, in terms of hardware, means having rapid access to large memory blocks. Therefore, one of the key focal areas of TEMPO are energy efficient non-volatile emerging memory technologies and novel ways to design and process memory and processing blocks on chip.”
