The slave dancer / / Paula Fox

• Record Nr.: UNINA9910165097903321
• Autore: Fox Paula
• Pubbl/distr/stampa: New York, New York : , : Open Road Integrated Media, , 2016; 2016
• ISBN: 1-5040-3740-5
• Descrizione fisica: 1 online resource (88 pages)
• Disciplina: 813.54
• Soggetti: Slave trade
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Sommario/riassunto: Newbery Medal Winner: A young Louisiana boy faces the horrors of slavery when he is kidnapped and forced to work on a slave ship in this iconic novel.Thirteen-year-old Jessie Bollier earns a few pennies playing his fife on the docks of New Orleans. One night, on his way home, a canvas is thrown over his head and he's knocked unconscious. When he wakes up, Jessie finds himself aboard a slave ship, bound for Africa. There, the Moonlight picks up ninety-eight black prisoners, and the men, women, and children, chained hand and foot, are methodically crammed into the ship's hold. Jessie's job is to provide music for the slaves to dance to on the ship's deck--not for amusement but for exercise, as a way to to keep their muscles strong and their bodies profitable.Over the course of the long voyage, Jessie grows more and more sickened by the greed of the sailors and the cruelty with which the slaves are treated. But it's one final horror, when the Moonlight nears her destination, that will change Jessie forever.Set during the middle of the nineteenth century, when the illegal slave trade was at its height, The Slave Dancer not only tells a vivid and shocking story of adventure and survival, but depicts the brutality of slavery with unflinching historical accuracy.

Machine Learning Applications in Electronic Design Automation / / edited by Haoxing Ren, Jiang Hu

• Record Nr.: UNINA9910637722203321
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
• ISBN: 3-031-13074-X
• Edizione: [1st ed. 2022.]
• Descrizione fisica: 1 online resource (585 pages)
• Collana: Mathematics and Statistics Series
• Disciplina: 929.374; 621.38150285631
• Soggetti: Electronic circuits; Embedded computer systems; Electronic circuit design; Electronic Circuits and Systems; Embedded Systems; Electronics Design and Verification
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Introduction -- Analysis of Digital Design: Routability Optimization for Industrial Designs at Sub-14nm Process Nodes Using Machine Learning -- RouteNet: Routability Prediction for Mixed-size Designs Using Convolutional Neural Network -- High Performance Graph Convolutional networks with Applications in Testability Analysis -- MAVIREC: ML-Aided Vectored IR-Drop Estimation and Classification -- GRANNITE: Graph Neural Network Inference for Transferable Power Estimation -- Machine Learning-Enabled High-Frequency Low-Power Digital Design Implementation at Advanced Process Nodes -- Optimization of Digital Design: Chip Placement with Deep Reinforcement learning -- DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement -- TreeNet: Deep Point Cloud Embedding for Routing Tree Construction -- Asynchronous Reinforcement Learning Framework for Net Order Exploration in Detailed Routing -- Standard Cell Routing with Reinforcement Learning and Genetic Algorithm in Advanced Technology Nodes -- PrefixRL: ; Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning -- GAN-CTS: A Generative Adversarial Framework for Clock Tree Prediction and Optimization -- Analysis and Optimization of Analog Design: Machine Learning Techniques in Analog Layout Automation -- Layout Symmetry Annotation for Analog Circuits with Graph Neural Networks -- ParaGraph: Layout parasitics and device parameter prediction using graph neural network -- GCN-RL circuit designer: Transferable transistor sizing with graph neural networks and reinforcement learn -- Parasitic-Aware Analog Circuit Sizing with Graph Neural Networks and Bayesian Optimization -- Logic and Physical Verification: Deep Predictive Coverage Collection/ Dynamically Optimized Test Generation Using Machine Learning -- Novelty-Driven Verification: Using Machine Learning to Identify Novel Stimuli and Close Coverage -- Using Machine Learning Clustering To Find Large Coverage Holes -- GAN-OPC: Mask optimization with lithography-guided generative adversarial nets -- Layout hotspot detection with feature tensor generation and deep biased learning.
• Sommario/riassunto: This book serves as a single-source reference to key machine learning (ML) applications and methods in digital and analog design and verification. Experts from academia and industry cover a wide range of the latest research on ML applications in electronic design automation (EDA), including analysis and optimization of digital design, analysis and optimization of analog design, as well as functional verification, FPGA and system level designs, design for manufacturing (DFM), and design space exploration. The authors also cover key ML methods such as classical ML, deep learning models such as convolutional neural networks (CNNs), graph neural networks (GNNs), generative adversarial networks (GANs) and optimization methods such as reinforcement learning (RL) and Bayesian optimization (BO). All of these topics are valuable to chip designers and EDA developers and researchers working in digital and analog designs and verification. Serves as a single-source reference to key machine learning (ML) applications and methods in digital and analog design and verification; Covers classical ML methods, as well as deep learning models such as convolutional neural networks (CNNs), graph neural networks (GNNs), generative adversarial networks (GANs) and optimization methods such as reinforcement learning (RL) and Bayesian optimization (BO); Discusses machine learning ML’s applications in electronic design automation (EDA), especially in the design automation of VLSI integrated circuits.