Memristor Neural Network Design

Neural network, a powerful learning model, has archived amazing results. However, the current Von Neumann computing system–based implementations of neural networks are suffering from memory wall and communication bottleneck problems ascribing to the Complementary Metal Oxide Semiconductor (CMOS) technology scaling down and communication gap. Memristor, a two terminal nanosolid state nonvolatile resistive switching, can provide energy‐efficient neuromorphic computing with its synaptic behavior. Crossbar architecture can be used to perform neural computations because of its high density and parallel computation. Thus, neural networks based on memristor crossbar will perform better in real world applications. In this chapter, the design of different neural network architectures based on memristor is introduced, including spiking neural networks, multilayer neural networks, convolution neural networks, and recurrent neural networks. And the brief introduction, the architecture, the computing circuits, and the training algorithm of each kind of neural networks are presented by instances. The potential applications and the prospects of memristor‐based neural network system are discussed

The curriculum reform of CAD graphic design for combining theory and practice

As a tool course, CAD graphic design in engineering is assisting other professional courses to achieve professional training goals. According to the characteristics and existing problems of CAD graphic design, this paper puts forward some constructive measures to connect the course with practice application and improve the students’ learning enthusiasm. The proposed measures include: teaching method combining theory with practice, teaching mode containing "teaching" and "learning" content, the matched evaluation mechanism guiding correctly students to learn

Equilibrium price and optimal insider trading strategy under stochastic liquidity with long memory

In this paper, the Kyle model of insider trading is extended by characterizing the trading volume with long memory and allowing the noise trading volatility to follow a general stochastic process. Under this newly revised model, the equilibrium conditions are determined, with which the optimal insider trading strategy, price impact and price volatility are obtained explicitly. The volatility of the price volatility appears excessive, which is a result of the fact that a more aggressive trading strategy is chosen by the insider when uninformed volume is higher. The optimal trading strategy turns out to possess the property of long memory, and the price impact is also affected by the fractional noise.Comment: 21 pages; 2 figure