Skip to main content

Quantum Engineering for Machine Learning

Periodic Reporting for period 1 - QUEFORMAL (Quantum Engineering for Machine Learning)

Reporting period: 2019-01-01 to 2019-12-31

The present reality of artificial intelligence (AI) applications is that algorithms run on huge data centers, consuming large amounts of energy. This reality is not adequate to respond to an emerging need: more and more applications requiring video and audio recognition, autonomous vehicles and robots, would benefit from cognitive functions performed locally (in ‘embedded systems’, technically), in order to reduce latency in cases in which delays are critical and in scenarios where a fast data connection is not available.

It has been recently shown that processor performance and memory energy efficiency cannot keep up with the exponentially increasing size and computational capacity required in deep neural networks (DNNs), one of most successful techniques for artificial intelligence. To win such challenges, significant improvements are required in device technology, architecture, and algorithms.

A possible solution pursued in the hardware AI community is moving to near-data processing architectures, in which computation is distributed in an array of Processing Elements, each containing both logic and memory, through which data flow to be processed. Use of embedded non-volatile memory is also pursued in order to further reduce energy consumption.

The vision proposed within the QUEFORMAL project surpasses both the present paradigm and the paradigms under development: Quantum engineering of transistors based on lateral and vertical heterostructures of 2D materials unifies materials science and device engineering exploiting the degrees of freedom provided by such materials to fabricate – on the same platform and in close vicinity – both low-voltage transistors and non-volatile memories.

The overall objective and targeted breakthrough of QUEFORMAL is to demonstrate the fabrication and operation of devices for logic-in-memory integrated circuits based on vertical and lateral heterostructures of 2DMs and to show the potential of this technology for integrated circuits for embedded machine learning capabilities.

QUEFORMAL pursues a very risky and original target, with the extremely high potential gain of advancing a science-enabled technology for the fabrication of integrated circuits for machine learning, in a field in which Europe has strong basic-science leadership, thanks to the pioneering breakthroughs on graphene and 2D materials.
In the first year of the project, progress has been made along many different lines of research:
- Materials: We have fabricated lateral heterostructures of 2D materials based on semimetallic and semiconducting PtSe2, and have improved the quality of homogeneous 2D materials to be used for the fabrication of lateral and vertical heterostructures grown by MOCVD and CVD.
- Devices: We have obtained the first promising experimental results on non-volatile memories based on lateral and vertical heterostructures with a good programming window.
- Modeling: Our multi-scale simulation framework for materials and devices based on 2D heterostructures has been expanded through the development of methods for modeling the electronic structure and the transport properties of lateral and vertical heterostructures and of new modules to simulate transport in multi-terminal devices.
- Circuit Design: We have designed a demonstrator of an analog vector-matrix multiplier in CMOS technology in order to validate our analog neural network architectural concept on proven technology.
We are advancing the state of the art in the fabrication of heterostructures of two-dimensional materials and of transistors and memories based such heterostructures. We are on track to demonstrate - by the end of the project - high-performance transistors and non-volatile memories fabricated in close vicinity and to validate the architectural concept of an analog deep neural network based on such devices.
QUEFORMAL concept for integrated transistors and non-volatile memory based on 2D materials