n-ary spintronics-based edge computing co-processor for artificial intelligence

Introduction

The rise of technologies such as the Internet of Things (IoT), autonomous vehicles, smart cameras, etc. is generating lots of big data. The volume of data in 2022 was 97ZB and is doubling every 2-3 years. This is leading to unprecedented growth in energy consumption and costs needed for data processing.

Challenges in Data Processing

Sending raw data for remote processing on centralized nodes is limited in terms of speed and bandwidth. Even next-gen tech like 5G or 6G will be insufficient to cope with this growth. Processing data at the Edge, where it's generated, requires increasing power efficiency by several orders of magnitude.

Limitations of Current Technology

However, the use of general-purpose digital processors based on von Neumann architecture is limited, with optimization possibilities nearing natural limits.

The Need for Neuromorphic Hardware

A new class of chips, neuromorphic hardware, is needed to execute AI algorithms like Deep Learning at high speed, low energy consumption, endurance, and scalability.

MultiSpin.AI's Vision

MultiSpin.AI's vision is to improve neuromorphic computing by:

1. Increasing energy efficiency and processing speed by at least three orders of magnitude over digital computing.
2. Achieving more than 10x improvement compared to the most advanced neuromorphic devices.
3. Reaching an unparalleled 2,000 Tera operations per second per watt (TOPS/W).

Development of AI Co-Processor

To achieve this, MultiSpin.AI will develop an AI co-processor based on a crossbar of multi-level magnetic tunnel junctions (M2TJ) cells/n-ary state cells.

Advantages of Multi-Level M2TJs

The use of multi-level M2TJs offers several benefits:

• Reduces the number of cells.
• Simplifies circuitry.
• Reduces the number of digital-to-analog conversions (DAC) at the input of the crossbar.
• Reduces analog-to-digital conversions at the crossbar output.

The combined effect is realizing much higher energy efficiency and faster AI inference at the Edge.

Impact of the Breakthrough

This breakthrough will help provide a significant impact by enabling transformative applications like:

• Autonomous vehicles
• Robots
• Medical devices

It will also help strengthen strategic autonomy for the EU chips industry and reduce CO2 emissions from AI inference.