Ever since the introduction of high bandwidth memory (HBM DRAM) and its succeeding line-ups, HBM DRAM has been heralded as a prominent solution to tackle the memory wall problem. However, despite ...continual memory advancements the advent of high-end systems, including supercomputers, hyper-scale data centers and machine learning accelerators, are expediting requirements for higher-performance memory solutions. To accommodate the increasing system-level demands, we introduce HBM3 DRAM, which employs multiple new features and design schemes. Techniques such as an on-die ECC engine, internal NN-DFE I/O signaling, TSV auto-calibration, and layout optimization based on machine-learning algorithms are implemented to efficiently control timing skew margins and SI degradation trade-offs. Furthermore, reduced voltage swings allow for improved memory bandwidth, density, power efficiency and reliability.
An inexpensive and effective technique based on machine learning (ML) algorithms with impedance characterization to sense and classify mixed gases is presented. Specifically, this method demonstrates ...that ML algorithms can distinguish hidden and valuable feature information such as different gas molecules from surface‐charged activated carbon fibers. The feature information used for ML is obtained by measuring the impedance and fitting the measured values to an equivalent circuit model. The mixed gases are classified using such feature information to train various automatic classifiers. The collected data consist of the resistances and capacitances extracted from best fitting results in Cole–Cole plots, and they are 5D vectors. The data processed with unsupervised learning are clustered, evaluated with Silhouette scores, and then the unique hidden patterns of individual gases in the mixed gases are obtained. When the supervised ML algorithm, k‐nearest neighbor classifier, is used for the analytical features, all combinations of gases have 94% classification accuracy, demonstrating the superiority of the proposed technique.
This study introduces a technique for efficiently classifying various mixed gases through activated carbon fiber using machine learning and impedance analysis. The hidden characteristics are confirmed by clustering and Silhouette score, which are unsupervised learning, and classified using various classifiers, which are supervised learning. Consequently, this research is anticipated to provide an efficient method to classify mixed gases.
