There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

On robust estimation of the location parameter

Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without sacrificing application accuracy or increasing hardware cost are critical to the wide deployment of DNNs in AI systems. This article aims to provide a comprehensive tutorial and survey about the recent advances toward the goal of enabling efficient processing of DNNs. Specifically, it will provide an overview of DNNs, discuss various hardware platforms and architectures that support DNNs, and highlight key trends in reducing the computation cost of DNNs either solely via hardware design changes or via joint hardware design and DNN algorithm changes. It will also summarize various development resources that enable researchers and practitioners to quickly get started in this field, and highlight important benchmarking metrics and design considerations that should be used for evaluating the rapidly growing number of DNN hardware designs, optionally including algorithmic codesigns, being proposed in academia and industry. The reader will take away the following concepts from this article: understand the key design considerations for DNNs; be able to evaluate different DNN hardware implementations with benchmarks and comparison metrics; understand the tradeoffs between various hardware architectures and platforms; be able to evaluate the utility of various DNN design techniques for efficient processing; and understand recent implementation trends and opportunities.

Efficient Processing of Deep Neural Networks: A Tutorial and Survey

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

We suggest that the productivity of FPGA users can be improved by adopting design libraries which are optimally implemented, rich in variety, easy to use, compatible with incremental development techniques and carefully validated. These requirements motivate our research into a framework for developing FPGA libraries involving the industrial-standard VHDL language and the declarative language Ruby. This paper describes the main elements in our framework, and illustrates its application to the Xilinx 6200 series FPGAs.

A Framework for Developing Parameterised FPGA Libraries

This paper explores methods for hardware acceleration of hidden Markov model (HMM) decoding for the detection of persons in still images. Our architecture exploits the inherent structure of the HMM trellis to optimise a Viterbi decoder for extracting the state sequence front observation features. Further performance enhancement is obtained by computing the HMM trellis states in parallel. The resulting hardware decoder architecture is mapped onto a field programmable gate array (FPGA). The performance and resource usage of our design is investigated for different levels of parallelism. Performance advantages over software are evaluated. We show how this work contributes to a real-time system for person-tracking in video-sequences.

/pdf/hardware-acceleration-of-hidden-markov-model-decoding-for-xfcb4z63wm.pdf

Hardware Acceleration of Hidden Markov Model Decoding for Person Detection

One of the most essential and challenging components in climate modeling is the atmospheric model. To solve multiphysical atmospheric equations, developers have to face extremely complex stencil kernels that are costly in terms of both computing and memory resources. This article aims to accelerate the solution of global shallow water equations (SWEs), which is one of the most essential equation sets describing atmospheric dynamics. We first design a hybrid methodology that employs both the host CPU cores and the field-programmable gate array (FPGA) accelerators to work in parallel. Through a careful adjustment of the computational domains, we achieve a balanced resource utilization and a further improvement of the overall performance. By decomposing the resource-demanding SWE kernel, we manage to map the double-precision algorithm into three FPGAs. Moreover, by using fixed-point and reduced-precision floating point arithmetic, we manage to build a fully pipelined mixed-precision design on a single FPGA, which can perform 428 floating-point and 235 fixed-point operations per cycle. The mixed-precision design with four FPGAs running together can achieve a speedup of 20 over a fully optimized design on a CPU rack with two eight-core processorsand is 8 times faster than the fully optimized Kepler GPU design. As for power efficiency, the mixed-precision design with four FPGAs is 10 times more power efficient than a Tianhe-1A supercomputer node.

/pdf/solving-the-global-atmospheric-equations-through-17ptxg8f0k.pdf

Solving the Global Atmospheric Equations through Heterogeneous Reconfigurable Platforms

This paper introduces the notion of a Flexible Instruction Processor (FIP) for systematic customisation of instruction processor design and implementation. The features of our approach include: (a) a modular framework based on “processor templates” that capture various instruction processor styles, such as stack-based or register-based styles; (b) enhancements of this framework to improve functionality and performance, such as hybrid processor templates and superscalar operation; (c) compilation strategies involving standard compilers and FIP-specific compilers, and the associated design flow; (d) technology-independent and technology-specific optimisations, such as techniques for ecient resource sharing in FPGA implementations. Our current implementation of the FIP framework is based on a highlevel parallel language called Handel-C, which can be compiled into hardware. Various customised Java Virtual Machines and MIPS style processors have been developed using existing FPGAs to evaluate the eectiveness and promise of this approach.

/pdf/flexible-instruction-processors-3xx0khlo7r.pdf

Flexible instruction processors

Dynamic routing can substantially enhance the quality of service for multiprocessor communication, and can provide intelligent adaptation of faulty links during run time. Implementing dynamic routing on a network-on-chip (NoC) platform requires a design that provides highly efficient optimal path computation coupled with reduced area and power consumption. In this paper, we present a hybrid analog-digital routing network design that enables efficient dynamic routing on an NoC architecture. The digital part provides accurate real-time traffic estimation using a temporal cost evaluation and adaptation scheme. The analog network, which is distributed within the digital communication network, provides an efficient implementation for the optimal routing algorithm with extremely low power consumption. Our results demonstrate the effectiveness of the hybrid analog-digital design, with a significant improvement in latency over the static routing for random hot spot traffics

http://cas.ee.ic.ac.uk/people/nps/papers/mak07noc.pdf

Wayne Luk

Papers

A Framework for Developing Parameterised FPGA Libraries

Hardware Acceleration of Hidden Markov Model Decoding for Person Detection

Solving the Global Atmospheric Equations through Heterogeneous Reconfigurable Platforms

Flexible instruction processors

A Hybrid Analog-Digital Routing Network for NoC Dynamic Routing