Interfacial thermal conductance in 2D WS2/MoSe2 and MoS2/MoSe2 lateral heterostructures

As machine learning (ML) workloads continue to scale, the demand for higher-density DRAM (Dynamic Random-Access Memory) and SRAM (Static Random-Access Memory) is intensifying, and also driving interest in compute-in-memory (CIM) architectures as a promising approach to enhancing computational efficiency. However, increasing DRAM cell density introduces significant challenges, particularly in the reliable identification of charge sharing between cells and bit lines. This challenge heightens the susceptibility of DRAM to row hammer attacks, where unintended data corruption occurs due to repeated access to adjacent rows. Furthermore, CIM architectures necessitate more precise sensing of bit lines to ensure accurate computation within memory. In light of these challenges, our study proposes an investigation into the potential benefits of utilizing an offset compensation sense amplifier (OCSA). The OCSA is designed to address the accuracy limitations posed by high-density DRAM in CIM applications. By enhancing the precision of bit line sensing, the OCSA may mitigate the vulnerability to row hammer attacks and improve the overall reliability and performance of CIM architectures. This study will explore the effectiveness of the OCSA in maintaining data integrity and computational accuracy within high-density DRAM environments, offering insights into its applicability for future ML workloads.

A Better Sense Amplifier Improves the Resilience in Compute-In-Memory and Row Hammer

Zhang,

Xu,

Wang

2024

Facial Expression Recognition Based on the FER2013 Dataset

Lou,

2024

Facial emotions are a way to express one's thoughts and also an effective way to understand the emotions of others. Nowadays, with the rapid development of technology, computers can also recognize facial expressions through convolutional neural networks, deep learning, and other methods, and classify the results. Throughout the entire experiment, we chose FER2013 data as the training set for our model, which ultimately achieved an accuracy of around 62%. We also compared it with the SFEW dataset. The emergence of facial expression recognition will increase in the future, and its application in teaching supervision is what we are exploring here. Its main function can be used for invigilation, attendance, checking class status, and so on.

Matting Image Background Switching Application

Shen,

Zhong

2024

With the development of the times, people are increasingly pursuing an ideal lifestyle, and frequent photography often makes it difficult for people to get rid of the modified "perfect" image in the images. Crop, as a commonly used image processing technique, plays an important role in beautifying images. This article will analyze and compare this image cropping technique in depth, and briefly introduce its application methods. From a technical perspective, this cropping algorithm is independent of Trimap technology. By performing mask cropping on the image, it effectively extracts details and ultimately generates a more natural image with background changes.