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From the Publisher:
This work covers all aspects of physical design. The book is a core reference for graduate students and CAD professionals. For students, concept and algorithms are presented in an intuitive manner. For CAD professionals, the material presents a balance of theory and practice. An extensive bibliography is provided which is useful for finding advanced material on a topic. At the end of each chapter, exercises are provided, which range in complexity from simple to research level.

Algorithms for VLSI Physical Design Automation

Materials whose optical properties can be reconfigured are crucial for photonic applications such as optical memories. Phase-change materials offer such utility and here recent progress is reviewed. Phase-change materials (PCMs) provide a unique combination of properties. On transformation from the amorphous to crystalline state, their optical properties change drastically. Short optical or electrical pulses can be utilized to switch between these states, making PCMs attractive for photonic applications. We review recent developments in PCMs and evaluate the potential for all-photonic memories. Towards this goal, the progress and existing challenges to realize waveguides with stepwise adjustable transmission are presented. Colour-rendering and nanopixel displays form another interesting application. Finally, nanophotonic applications based on plasmonic nanostructures are introduced. They provide reconfigurable, non-volatile functionality enabling manipulation and control of light. Requirements and perspectives to successfully implement PCMs in emerging areas of photonics are discussed.

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Phase-change materials for non-volatile photonic applications

AutoDock Vina is arguably one of the fastest and most widely used open-source programs for molecular docking. However, compared to other programs in the AutoDock Suite, it lacks support for modeling specific features such as macrocycles or explicit water molecules. Here, we describe the implementation of this functionality in AutoDock Vina 1.2.0. Additionally, AutoDock Vina 1.2.0 supports the AutoDock4.2 scoring function, simultaneous docking of multiple ligands, and a batch mode for docking a large number of ligands. Furthermore, we implemented Python bindings to facilitate scripting and the development of docking workflows. This work is an effort toward the unification of the features of the AutoDock4 and AutoDock Vina programs. The source code is available at https://github.com/ccsb-scripps/AutoDock-Vina.

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AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings.

The main characteristic of Reconfigurable Computing is the presence of hardware that can be reconfigured to implement specific functionality more suitable for specially tailored hardware than on a simple uniprocessor. Reconfigurable computing systems join microprocessors and programmable hardware in order to take advantage of the combined strengths of hardware and software and have been used in applications ranging from embedded systems to high performance computing. Many of the fundamental theories have been identified and used by the Hardware/Software Co-Design research field. Although the same background ideas are shared in both areas, they have different goals and use different approaches.This book is intended as an introduction to the entire range of issues important to reconfigurable computing, using FPGAs as the context, or "computing vehicles" to implement this powerful technology. It will take a reader with a background in the basics of digital design and software programming and provide them with the knowledge needed to be an effective designer or researcher in this rapidly evolving field.

· Treatment of FPGAs as computing vehicles rather than glue-logic or ASIC substitutes
· Views of FPGA programming beyond Verilog/VHDL
· Broad set of case studies demonstrating how to use FPGAs in novel and efficient ways

Reconfigurable Computing: The Theory and Practice of FPGA-Based Computation

Clustering of a large number of data points is a computational demanding task that often needs the be accelerated in order to be useful in practice. The focus of this work is on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, which is one of the state-of-the-art clustering algorithms, targeting its acceleration using an FPGA device. The paper presents a novel, optimised and scalable architecture that takes advantage of the internal memory structure of modern FPGAs in order to deliver a high performance clustering system. Results show that the developed system can obtain average speed-ups of 32x in real-world tests and 202x in synthetic tests when compared to state-of-the-art software counterparts.

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Reconfigurable Computing: Architectures, Tools and Applications

AutoDock4 is a widely used program for docking small molecules to macromolecular targets. It describes ligand-receptor interactions using a physics-inspired scoring function that has been proven useful in a variety of drug discovery projects. However, compared to more modern and recent software, AutoDock4 has longer execution times, limiting its applicability to large scale dockings. To address this problem, we describe an OpenCL implementation of AutoDock4, called AutoDock-GPU, that leverages the highly parallel architecture of GPU hardware to reduce docking runtime by up to 350-fold with respect to a single-threaded process. Moreover, we introduce the gradient-based local search method ADADELTA, as well as an improved version of the Solis-Wets random optimizer from AutoDock4. These efficient local search algorithms significantly reduce the number of calls to the scoring function that are needed to produce good results. The improvements reported here, both in terms of docking throughput and search efficiency, facilitate the use of the AutoDock4 scoring function in large scale virtual screening.

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Accelerating AutoDock4 with GPUs and Gradient-Based Local Search.

This work discusses the Memory Architecture for Reconfigurable Computers (MARC), a scalable, device-independent memory interface that supports both irregular (via configurable caches) and regular accesses (via prefetching stream buffers). By hiding specifics behind a consistent abstract interface, it is suitable as a target environment for automatic hardware compilation.

Memory Access Schemes for Configurable Processors

Future high-performance computing systems will need to include multiple specialized accelerators in a single heterogeneous system to overcome power-density limitations of CPU performance.

OpenMP device offloading to FPGA accelerators

This paper presents a very efficient approach for algorithms developed based on conformal geometric algebra using reconfigurable hardware. We use the inverse kinematics of the arm of a virtual human as an example, but we are convinced that this approach can be used in a wide field of computer animation applications. We describe the original algorithm on a very high geometrically intuitive level as well as the resulting optimized algorithm based on symbolic calculations of a computer algebra system. The main focus then is to demonstrate our approach for the hardware implementation of this algorithm leading to a very efficient implementation.

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Andreas Koch

Papers

Reconfigurable Computing: Architectures, Tools and Applications

Accelerating AutoDock4 with GPUs and Gradient-Based Local Search.

Memory Access Schemes for Configurable Processors

OpenMP device offloading to FPGA accelerators

EFFICIENT INVERSE KINEMATICS ALGORITHM BASED ON CONFORMAL GEOMETRIC ALGEBRA - Using Reconfigurable Hardware