As Moore’s law slows down, foundries and design houses are resorting to design technology co-optimization (DTCO) to squeeze more performance out of a technology node. However, the efficiency of EDA tools plays a key role in driving DTCO. The existing commercial parasitic extraction tools are not able to respond to a process parameter change efficiently. For example, it takes 25 minutes to re-generate a parasitic netlist for a mere layer thickness change using existing commercial tools. This runtime becomes the bottleneck for standard cell DTCO. In this work, we demonstrate a machine-learning-based method targeted on standard cells that can efficiently extract parasitic capacitance within seconds while maintaining competitive error distribution compared to the state-of-the-art rule-based 2.5D extraction method, which suffers from pattern mismatch since no real layout is provided at the pre-characterization stage. We extract patterns from actual standard cell layouts as training data for the ML model, and the model can predict coupling capacitance with unseen layer thickness within milliseconds.

In this paper, we aim to automatically discover high-quality frame-level speech features and acoustic tokens directly from unlabeled speech data. A multigranular acoustic tokenizer (MAT) was proposed for automatic discovery of multiple sets of acoustic tokens from the given corpus. Each acoustic token set is specified by a set of hyperparameters describing the model configuration. These different sets of acoustic tokens carry different characteristics for the given corpus and the language behind and, thus, can be mutually reinforced. The multiple sets of token labels are then used as the targets of a multitarget deep neural network (MDNN) trained on frame-level acoustic features. Bottleneck features extracted from the MDNN are then used as the feedback input to the MAT and the MDNN itself in the next iteration. The multigranular acoustic token sets and the frame-level speech features can be iteratively optimized in the iterative deep learning framework. We call this framework the MAT deep neural network. The results were evaluated using the metrics and corpora defined in the Zero Resource Speech Challenge organized at Interspeech 2015, and improved performance was obtained with a set of experiments of query-by-example spoken term detection on the same corpora. Visualization for the discovered tokens against the English phonemes was also shown.

In this work we aim to discover high quality speech features and Linguistic units directly from unlabeled speech data in a zero resource scenario. The results are evaluated using the metrics and corpora proposed in the Zero Resource Speech Challenge organized at Interspeech 2015. A Multi-layered Acoustic Tokenizer (MAT) was proposed for automatic discovery of multiple sets of acoustic tokens from the given corpus. Each acoustic token set is specified by a set of hyperparameters that describe the model configuration. These sets of acoustic tokens carry different characteristics fof the given corpus and the language behind, thus can be mutually reinforced. The multiple sets of token labels are then used as the targets of a Multi-target Deep Neural Network (MDNN) trained on low-level acoustic features. Bottleneck features extracted from the MDNN are then used as the feedback input to the MAT and the MDNN itself in the next iteration. We call this iterative deep learning framework the Multi-layered Acoustic Tokenizing Deep Neural Network (MAT-DNN), which generates both high quality speech features for the Track 1 of the Challenge and acoustic tokens for the Track 2 of the Challenge. In addition, we performed extra experiments on the same corpora on the application of query-by-example spoken term detection. The experimental results showed the iterative deep learning framework of MAT-DNN improved the detection performance due to better underlying speech features and acoustic tokens.

Due to the advancement of technology, the phenomenon of using digital teaching materials during the process of learning has spread among various subjects. The aim of this study is to understand the fundamental factors influencing students' acceptance of applying digital recording technology to prepare and present research papers in college courses. Sixty-nine questionnaires were distributed and sixty-seven usable questionnaires were returned. The design of questionnaire was based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model which consisted of scales of performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), social influence (SI), behavioral intention (BI) and use behavior (UB) to assess the acceptance of students. Practical implications for students can be drawn from these findings in terms of developing e-Learning, u-Learning and beyond.

A large number of learning systems have applied touch elements to fulfill their user experience. eBook, another well-known e-Learning material, has been developed quickly and plays an important role nowadays. Nevertheless, there are not any learning systems designed for editing an e-Book including multi-touch contents. This motivates us to find a novel approach to design hierarchical teaching materials of digital learning content and e-Book. Due to the multi-touch technology and activity tree structure applied in this paper, this proposed approach can significantly enhance the variety of teaching strategy and interactivity of students. The designed approach consists of two parts: editor and player. About the editor, instruction designers can load multimedia content on a haptic tree node, insert search pattern to the node, and then output it as a project. About the player, learners can load the project and then touch every node on the tree.