Fault Injection Attack Emulation Framework for Early Evaluation of IC Designs

Fault injection attack (FIA) has become a serious threat to the confidentiality and fault tolerance of integrated circuits (ICs). Circuit designers need an effective method to evaluate the countermeasures of the IC designs against the FIAs at the design stage. To address the need, this article, based on FPGA emulation, proposes an in-circuit early evaluation framework, in which FIAs are emulated with parameterized fault models. To mimic FIAs, an efficient scan approach is proposed to inject faults at any time at any circuit nodes, while both the time and area overhead of fault injection are reduced. After the circuit design under test (CUT) is submitted to the framework, the scan chains insertion, fault generation, and fault injection are executed automatically, and the evaluation result of the CUT is generated, making the evaluation a transparent process to the designers. Based on the framework, the confidentiality and fault-tolerance evaluations are demonstrated with an information-based evaluation approach. Experiment results on a set of ISCAS89 benchmark circuits show that on average, our approach reduces the area overhead by 41.08% compared with the full scan approach and by over 20.00% compared with existing approaches. The confidentiality evaluation experiments on AES-128 and DES-56 and the fault-tolerance evaluation experiments on two CNN circuits, a RISC-V core, a Cordic core, and the float point arithmetic units show the effectiveness of the proposed framework.

Circuit engineering methods for ensuring ECB resistance to the effects of heavy charged particles

The paper considers circuit engineering methods for protecting the electronic component base from the effects of heavy charged particles. One of the main methods is to increase the capacity of the device, which leads to an increase in the capacity of diffusion regions and a decrease in the frequency of single events. The structure of a capacitor is shown, which is connected to various nodes of the circuit to increase the sensitivity of the capacitance of the node. The article focuses on the method of using active RC circuits in the feedback circuit of a storage device cell. The advantages and disadvantages of the methods of using a storage device cell with internal redundancy are noted. The paper shows that the use of circuit engineering methods will provide the required level of fault and fault tolerance to the effects of heavy charged particles.

Application of Multilayer Neural Network in Sports Psychology

The research on multilayer neural network theory has developed rapidly in recent years. It has parallel processing capabilities and fault tolerance and has aroused the interest of many researchers. The neural network has made great progress in the field of control, especially in model identification and control. It has been quickly applied in the fields of device design, optimized operation, and fault analysis and diagnosis. Neural network control, as an automated control technology in the 21st century, has been fully proved by theories and practices at home and abroad, and it is very useful in complex process control. Sports psychology is a discipline that studies the psychological characteristics and laws of people engaged in sports, and it is also a new development in sports science. The main task of sports psychology is to study people’s psychological processes when participating in sports, such as feeling, perception, appearance, thinking, memory, emotion, and characteristics of will and its role and significance in sports. An important feature of multilayer neural networks is to achieve results that match the expected output through network learning. It has strong self-learning, self-adaptability, and fault tolerance. The multilayer neural network system evaluation method is unique with its extraordinary ability to deal with complex nonlinear problems and does not involve human intervention. This article presents a multilayer neural network algorithm, which classifies the samples of athletes, and studies the physical education training process, the psychological characteristics of related personnel in sports competitions, such as the psychological characteristics of the formation of sports skills, and the psychological training of athletes before the game.

Fault Tolerance and Noise Immunity in Freespace Diffractive Optical Neural Networks

Free-space diffractive optical networks are a class of trainable optical media that are currently being explored as a novel hardware platform for neural engines. The training phase of such systems is usually performed in a computer and the learned weights are then transferred onto optical hardware ("ex-situ training"). Although this process of weight transfer has many practical advantages, it is often accompanied by performance degrading faults in the fabricated hardware. Being analog systems, these engines are also subject to performance degradation due to noises in the inputs and during optoelectronic conversion. Considering diffractive optical networks (DON) trained for image classification tasks on standard datasets, we numerically study the performance degradation arising out of weight faults and injected noises and methods to ameliorate these effects. Training regimens based on intentional fault and noise injection during the training phase are only found marginally successful at imparting fault tolerance or noise immunity. We propose an alternative training regimen using gradient based regularization terms in the training objective that are found to impart some degree of fault tolerance and noise immunity in comparison to injection based training regimen.

QoS Aware and Fault Tolerance Based Software-Defined Vehicular Networks Using Cloud-Fog Computing

Software-defined network (SDN) and vehicular ad-hoc network (VANET) combined provided a software-defined vehicular network (SDVN). To increase the quality of service (QoS) of vehicle communication and to make the overall process efficient, researchers are working on VANET communication systems. Current research work has made many strides, but due to the following limitations, it needs further investigation and research: Cloud computing is used for messages/tasks execution instead of fog computing, which increases response time. Furthermore, a fault tolerance mechanism is used to reduce the tasks/messages failure ratio. We proposed QoS aware and fault tolerance-based software-defined V vehicular networks using Cloud-fog computing (QAFT-SDVN) to address the above issues. We provided heuristic algorithms to solve the above limitations. The proposed model gets vehicle messages through SDN nodes which are placed on fog nodes. SDN controllers receive messages from nearby SDN units and prioritize the messages in two different ways. One is the message nature way, while the other one is deadline and size way of messages prioritization. SDN controller categorized in safety and non-safety messages and forward to the destination. After sending messages to their destination, we check their acknowledgment; if the destination receives the messages, then no action is taken; otherwise, we use a fault tolerance mechanism. We send the messages again. The proposed model is implemented in CloudSIm and iFogSim, and compared with the latest models. The results show that our proposed model decreased response time by 50% of the safety and non-safety messages by using fog nodes for the SDN controller. Furthermore, we reduced the execution time of the safety and non-safety messages by up to 4%. Similarly, compared with the latest model, we reduced the task failure ratio by 20%, 15%, 23.3%, and 22.5%.