Smart hardware architecture with random weight elimination and weight balancing algorithms

Reducing the number of connections in hardware artificial neural networks, as compared with their software counterparts, can result in a drastic reduction in costs, because the reduction translates into utilizing fewer devices. This paper presents the demonstration of a method, by using simulations, to halve the amount of weights in a network while minimizing the accuracy loss. Additionally, the appropriate considerations for translating these simulation results to hardware networks are also detailed

Heuristic model for configurable polymer wire synaptic devices

Recently, there has been considerable research on nonvolatile analog devices for artificial intelligence (AI); however, it focuses on all-coupled neural networks. In contrast, polymer wire-type synaptic devices, which can be expected to be arbitrarily wired similar to a biological neural network, have already been proposed and demonstrated. In this study, we model a polymer wire synaptic device based on the results of previous research, and demonstrate an example of applying simple perceptron (AI) to the model. The results of our study show that it is possible to predict effective methods of using polymer wire synaptic elements in AI

Feasibility of Rapid Diagnostic Technology for SARS-CoV-2 Virus Using a Trace Amount of Saliva

Containment of SARS-CoV-2 has become an urgent global issue. To overcome the problems of conventional quantitative polymerase chain reaction (qPCR) tests, we verified the usefulness of a mobile qPCR device that utilizes mouthwash to obtain a saliva sample with the aim of developing a rapid diagnostic method for SARS-CoV-2. First, we examined whether anyone could easily operate this device. Then, we examined whether RNA in the mouthwash could be detected in a short time. In addition, we investigated whether it was possible to diagnose SARS-CoV-2 infection using mouthwash obtained from COVID-19 patients undergoing hospitalization. The results revealed that all subjects were able to complete the operation properly without error. In addition, RNase P was detected in the mouthwash without pretreatment. The average detection time was 18 min, which is significantly shorter than conventional qPCR devices. Furthermore, this device detected SARS-CoV-2 in the mouthwash of a COVID-19 patient undergoing hospitalization. The above findings verified the efficacy of this diagnostic method, which had a low risk of infection, was technically simple, and provided stable results. Therefore, this method is useful for the rapid detection of SARS-CoV-2

Improving the Detection Sensitivity of a New Rapid Diagnostic Technology for Severe Acute Respiratory Syndrome Coronavirus 2 Using a Trace Amount of Saliva

The early diagnosis and isolation of infected individuals with coronavirus disease 2019 (COVID-19) remain important. Although quantitative polymerase chain reaction (qPCR) testing is considered the most accurate test available for COVID-19 diagnosis, it has some limitations, such as the need for specialized laboratory technicians and a long turnaround time. Therefore, we have established and reported a rapid diagnostic method using a small amount of saliva as a sample using a lightweight mobile qPCR device. This study aimed to improve the existing method and increase the detection sensitivity and specificity. The detection specificity of CDC N1 and N2 was examined by improving qPCR reagents and polymerase chain reaction conditions for the previously reported method. Furthermore, the feasibility of detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral RNA was examined using both the previous method and the improved method in patients with COVID-19. The results showed that the improved method increased the specificity and sensitivity. This improved method is useful for the rapid diagnosis of SARS-CoV-2