Memristors offer advantages as a hardware solution for neuromorphic computing, and however, their nonlinear device property makes the weight update inaccurately and reduces the recognition accuracy of a neural network. In this paper, a piecewise linear (PL) method is proposed to mitigate the nonlinear effect of memristors by calculating the weight update parameters along a piecewise line, which reduces the errors in the weight update process. It mitigates the nonlinearity impact without reading the precise conductance of the memristor in each updating step, thereby avoiding complex peripheral circuits. The effectiveness of the proposed PL method with 2-segment, 3-segment, and 4-segment models in two split selection strategies is investigated, and the impact of various variations is considered. The results show that under different nonlinearities, the PL method can increase the recognition accuracy of the Modified National Institute of Standards and Technology (MNIST) handwriting digits to 87.87%–95.05% compared with 10.77%–73.18% of the cases without the PL method.

Artificial intelligence (AI) has gained widespread public interest in recent years. However, as AI literacy remained excluded from the standard academic curricula, AI education in the US was predominantly offered through extra-curricular activities, which limited AI learning exposure to only a select group of students. Given these limitations, the need to integrate AI literacy education into the standard curricula is increasingly evident. This study investigated the integration of AI learning in an advanced biology course. Thirty-seven students participated in four lessons embedding AI learning in biology contexts. The interplay of students’ AI learning and biology knowledge was examined from the quantitative measure of conceptual understanding and qualitative analysis of interdisciplinary reasoning. This concurrent triangulation research design utilized results from both quantitative and qualitative analyses to develop a comprehensive understanding of students’ AI learning in the biology context. The results of the study showed a significant improvement in students’ AI concepts. Students’ biology knowledge had a slight increase, but it was not statistically significant. Both quantitative and qualitative results underscored a close connection between students’ AI learning and their biology knowledge, though the quantitative findings were not conclusive in some lessons. The article concluded with a discussion of the potential reasons for those discrepancies. In addition, suggestions were provided for future research and practitioners who are interested in integrating AI education across curricula.

The growing popularity of powerful mobile devices such as smart phones and tablet devices has resulted in the exponential growth of demand for video applications. However, due to the large video data size and intensive computation, mobile video applications require frequent embedded memory access, which consumes a large amount of power and limits battery life. In this paper, we present a low-cost self-recovery video storage system by investigating meaningful data patterns hidden in big video data, by introducing data mining techniques to the hardware design process. We propose a two-dimensional data-pattern approach to explore horizontal data-association and vertical data-correlation characteristics. Such data relationship discovery and pattern identification enable a new dimension for the hardware design space and bring self-recovery ability to memories in the presence of bitcell failures. Based on the identified optimal data patterns, we present a low-cost and efficient SRAM design to enable data self-recovery at low voltages. A 45nm 32kb SRAM is implemented that delivers good video quality at near-threshold voltage (0.5 V) with negligible area overhead (7.94%).

Mobile devices are becoming ever more popular for streaming videos, which account for the majority of all the data traffic on the Internet. Memory is a critical component in mobile video processing systems, increasingly dominating the power consumption. Today, memory designers are still focusing on hardware-level power optimization techniques, which usually come with significant implementation cost (e.g., silicon area overhead or performance penalty). In this paper, we propose a video content-aware memory technique for power-quality tradeoff from viewer's perspectives. Based on the influence of video macroblock characteristics on the viewer's experience, we develop two simple and effective models-decision tree and logistic regression to enable hardware adaptation. We have also implemented a novel viewer-aware bit-truncation technique which minimizes the impact on the viewer's experience, while introducing energy-quality adaptation to the video storage.

Pixel‐wise classification of hyperspectral image (HSI) is a hot spot in the field of remote sensing. The classification of HSI requires the model to be more sensitive to dense features, which is quite different from the modelling requirements of traditional image classification tasks. Cycle‐Multilayer Perceptron (MLP) has achieved satisfactory results in dense feature prediction tasks because it is an expert in extracting high‐resolution features. In order to obtain a more stable receptive field and enhance the effect of feature extraction in multiple directions, we propose an MLP‐like model called DriftNet for HSI classification inspired by Cycle‐MLP and deformable convolution. Besides, the proposed DriftNet uses a unique ladder‐like fully connected structure to achieve progressive activation of neurons and facilitates the fusion of spatial and spectral information, thereby obtaining more sensitive location information for better classification results. Experimental results on three public hyperspectral datasets demonstrate the effectiveness and generalisation of DriftNet.

Embedded memory is a critical component in today's mobile video processing systems, increasingly dominating power consumption and shortening battery life of mobile devices. Traditional hardware-level power optimization techniques usually come with significant implementation overhead to solve the memory failure problem during low-voltage operations. This paper presents a novel mobile video memory to exploit the power saving opportunities provided by a viewer experience under environmental visual interference. The viewing contexts, in particular the ambient luminance, significantly influence the quality of the viewer experience, and in the context with higher luminance levels, mobile users have higher tolerance to the video degradation. Accordingly, the memory failures can be introduced adaptively to achieve power savings without influencing the viewer experience. To meet the silicon area constraint in mobile devices, a simple but an efficient hardware implementation scheme is developed to minimize area overhead. The experimental results based on a 45-nm CMOS technology show that, as compared with the conventional memory design, the proposed technique can achieve up to 48% power savings with good perceivable quality and negligible implementation overhead.