Artificial Intelligence (AI) has demonstrated its transformative potential with applications in language models, medical diagnostics, industrial automation, and more. It has generated an astounding €2.7 trillion in business value in 2021 alone, and forecasts predict AI will generate €12T in value by 2030. However, as the demand for AI increases, existing computing architectures, constrained by their intrinsic limitations, are struggling to keep pace. Global data processing reached 97ZB in 2022, and this volume is doubling every two-three years, overwhelming the capabilities of even next-generation technologies like 5G and 6G.
MultiSpin.AI is set to innovate the world of AI through development of an n-ary spintronics-based edge computing co-processor. This technology aims to overcome the limitations of current computing hardware and enable AI to address some of the world’s most pressing global challenges, from healthcare to climate change, transportation, and food security.
Challenge: Overcoming the Limitations of Existing AI Hardware
The progress of current computing technology is impeded due to major obstacles:
1. Von Neumann Bottleneck: Traditional computing architectures separate processing and memory units, creating a data transfer bottleneck that slows down AI computations.
2. End of Moore’s and Dennard’s Laws: With transistor sizes shrinking to 2nm, the semiconductor industry is approaching physical limits, leaving little room for further improvements in computing power.
3. Energy Consumption: Information and communication technologies (ICT) are on track to consume up to 21% of global energy by 2030, posing a serious threat to sustainability goals.
Solution: New Era of Spintronics-based AI Co-processors
MultiSpin.AI uses spintronics-based technology to develop an edge computing co-processor designed for AI application. This co-processor will enhance energy efficiency, computational speed, and accuracy, overcoming the limitations of current hardware architectures. It will also enable AI to process data locally at the edge—closer to the source—rather than relying on centralized cloud computing, reducing latency, bandwidth, and energy consumption.
Project Objectives: Pushing the Boundaries of AI and Spintronics
The goal of MultiSpin.AI is to design, fabricate, and test AI inference with innovative n-ary multistate magnetic tunnel junctions. Thus, instead of existing binary magnetic tunnel junctions, these structures can switch between multiple magnetic states, providing unprecedented speed and energy efficiency for AI computations.
The strategy of MultiSpin.AI for achieving this goal is to operate in three levels with increasing complexity: (a) single layer multi -level structures (SLMMS) (b) multi-level magnetic tunnel junctions (M2TJ) where one of the magnetic layers of the junctions is a SLMMS, and (c) a crossbar of M2TJ that will be integrated into the proof-of-concept prototype. While we aspire to demonstrate 16 discrete magnetic states with the SLMMS, in the crossbar we will aim for M2TJs with 4 resistance states. Based on this strategy we will be able to demonstrate the potential of this technology not only based on the prototype but also on the expected performance of crossbars consisting of M2TJs with more than 4 resistance states.
The image attached to the report shows a bottom magnetic layer consisting of two crossing ellipses and a top magnetic layer consisting of a single ellipse. The multi resistance states of the M2TJ are related to the multiple magnetic states supported by the bottom magnetic layer.
