Keywords

Artificial neural networks, Optimization, Printed electronics, Design automation.

Background

Printed Electronics Due to the stretchability, non-toxicity, and ultra-low-cost, printed electronics (PE) dominates the innovative areas such as wearable technology and Internet of Things (IoT) infrastructures. In contrast to silicon-based technology, PE is additive manufacturing that allows high-degree customization and low-amount production, and thus has received considerable interest in edge-AI community.​

Artificial neural networks (ANNs) ANNs simulate the human nervous system and have a solid ability to deal with non-linear problems. Besides, the learning-based optimization allows them an extremely high efficiency in designing their parameters. Further, the ultra-simplicity of their computing units (weighted-sum & activation) are ideal for the hardware implementation in PE. A promising approach it is the printed neuromorphic computing system.

Printed neuromorphics Despite the availability of a large number of computing units in the field of ubiquitous computing and edge-AI (such as Arduino and mobile phones), the cost and extravagant computing resources can still be saved by printed neuromorphic systems. Printed neuromorphics are the hardware implementation of ANN using resistors, transistors, etc.

Goal of thesis To enhance the effectiveness and efficiency in manufacturing the printed neuromorphic, we prepared to training multiple networks simutanously and keep the majority of the corresponding components (resistors) in different nets at the same value.

Your jobs

• Research state of the art printed electronic technology

• Find the suitable datasets for split manufacturing

• Implement a training framework for printed neuromorphics

• Evaluate the proposed framework

• Write thesis

We provide you

• Chance & experience in field of artificial neural networks, design automation, and optimization.

• Intensive and interdisciplinary support

• A pleasant working atmosphere and constructive cooperation

• Possibility to publish this work as one of the co-authors

We expected

• Students major in Electrical Engineering, Mechatronics, Informatics, and other related majors

• Independent thinking and working

• Strong knowledge of Python (specifically Pytorch)

• Strong knowledge in artificial neural networks

Contact

If you are interested in this work, please contact Haibin Zhao (haibin.zhao@kit.edu).

Reference

Weller D D, Hefenbrock M, Beigl M, et al. Realization and training of an inverter-based printed neuromorphic computing system[J]. Scientific reports, 2021, 11(1): 1-13.