Search

Back propagation (BP) based on stochastic gradient descent is the prevailing method to train multilayer neural networks (MNNs) with hidden layers. However, the existence of the physical separation between memory arrays and arithmetic module makes it inefficient and ineffective to implement BP in conventional digital hardware. Although CMOS may alleviate some problems of the hardware implementation of MNNs, synapses based on CMOS cost too much power and areas in very large scale integrated circuits. As a novel device, memristor shows promises to overcome this shortcoming due to its ability to closely integrate processing and memory. This paper proposes a novel circuit for implementing a synapse based on a memristor and two MOSFET tansistors (p-type and n-type). Compared with a CMOS-only circuit, the proposed one reduced the area consumption by 92%–98%. In addition, we develop a fuzzy method for the adjustment of the learning rates of MNNs, which increases the learning accuracy by 2%–3% compared with a constant learning rate. Meanwhile, the fuzzy adjustment method is robust and insensitive to parameter changes due to the approximate reasoning. Furthermore, the proposed methods can be extended to memristor-based multilayer convolutional neural network for complex tasks. The novel architecture behaves in a human-liking thinking process. [ABSTRACT FROM AUTHOR]

Physical unclonable function (PUF) is a technique to produce secret keys or complete authentication in integrated circuits (ICs) by exploiting the uncontrollable randomness due to manufacturing process variations. For better PUF applications, efficient analysis of different designs is important. In this article, a mathematical model to analyze the performance of typical strong PUF designs is proposed and applied to arbiter PUF, ring oscillator (RO) PUF, and duty cycle (DC) PUF. For better reliability, a new PUF design, DC multiplexer (DC MUX) PUF proposed in our previous work is analyzed. The proposed model indicates that DC MUX PUF achieves 2% higher reliability than arbiter PUF under environment influences. It also shows that DC PUF achieves 10% higher reliability than RO PUF. For verification, the aforementioned four PUF designs are testified using HSPICE. As our model analysis indicates, for reliability DC MUX PUF outperforms arbiter PUF, and DC PUF outperforms RO PUF. For randomness, DC MUX PUF and DC PUF outperform arbiter PUF and RO PUF, respectively. For security, LR attacks on DC MUX PUF and arbiter PUF are performed. The training time for DC MUX PUF is 40 000 times of arbiter PUF. [ABSTRACT FROM AUTHOR]