Beihang University

About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Computer science & Control theory. The organization has 67002 authors who have published 73507 publications receiving 975691 citations. The organization is also known as: Beijing University of Aeronautics and Astronautics.

Spintronics: Emerging Ultra-Low-Power Circuits and Systems beyond MOS Technology

Abstract: Conventional MOS integrated circuits and systems suffer serve power and scalability challenges as technology nodes scale into ultra-deep-micron technology nodes (e.g., below 40nm). Both static and dynamic power dissipations are increasing, caused mainly by the intrinsic leakage currents and large data traffic. Alternative approaches beyond charge-only-based electronics, and in particular, spin-based devices, show promising potential to overcome these issues by adding the spin freedom of electrons to electronic circuits. Spintronics provides data non-volatility, fast data access, and low-power operation, and has now become a hot topic in both academia and industry for achieving ultra-low-power circuits and systems. The ITRS report on emerging research devices identified the magnetic tunnel junction (MTJ) nanopillar (one of the Spintronics nanodevices) as one of the most promising technologies to be part of future micro-electronic circuits. In this review we will give an overview of the status and prospects of spin-based devices and circuits that are currently under intense investigation and development across the world, and address particularly their merits and challenges for practical applications. We will also show that, with a rapid development of Spintronics, some novel computing architectures and paradigms beyond classic Von-Neumann architecture have recently been emerging for next-generation ultra-low-power circuits and systems.

A Highly Elastic and Reversibly Stretchable All-Polymer Supercapacitor.

Abstract: Multiple stretchability has never been demonstrated as supercapacitors because the hydrogel used cannot fully recover after being heavily deformed. Now, a highly reversibly stretchable all-polymer supercapacitor was fabricated using a developed double network hydrogel (DN hydrogel) as electrolyte and pure polypyrrole (PPy) as electrode. The DN hydrogel provides excellent mechanical properties, which can be stretched up to 500 % many times and then restore almost 100 % of the original length. To fabricate the fully recoverable stretchable supercapacitor, we annealed a free-standing pure conducting polymer film as electrode so that the electrodes induced retardance is minimized. The as-fabricated DN hydrogel/pure conducting polymer supercapacitors can be perfectly recovered from 100 % strain with almost no residual deformation left and the electrochemical performance can be maintained even after 1000 stretches (but not bending).

Cutting forces and surface finish when machining medium hardness steel using CBN tools

Abstract: Cutting forces generated using CBN tools have been evaluated when cutting steel being hardened to 45–55 HRC. Radial thrust cutting force was the largest among the three cutting force components and was most sensitive to the changes of cutting edge geometry and tool wear. The surface finish produced by CBN tools was compatible with the results of grinding and was affected by cutting speed, tool wear and plastic behaviour of the workpiece material.

BERT-of-Theseus: Compressing BERT by Progressive Module Replacing

Abstract: In this paper, we propose a novel model compression approach to effectively compress BERT by progressive module replacing. Our approach first divides the original BERT into several modules and builds their compact substitutes. Then, we randomly replace the original modules with their substitutes to train the compact modules to mimic the behavior of the original modules. We progressively increase the probability of replacement through the training. In this way, our approach brings a deeper level of interaction between the original and compact models. Compared to the previous knowledge distillation approaches for BERT compression, our approach does not introduce any additional loss function. Our approach outperforms existing knowledge distillation approaches on GLUE benchmark, showing a new perspective of model compression.