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」

This paper describes a method for customising the representation of floating-point numbers that exploits the flexibility of re-configurable hardware. The method determines the appropriate size of mantissa and exponent for each operation in a design, so that a cost functionn with a given error specification for the output relative to a reference representation can be satisfied. We adopt an iterative implementation of this method, which supports IEEE single-precision or double-precision floating-point representation as the reference representation. This implementation produces customised floating-point formats with arbitrary-sized mantissa and exponent. The tool follows a generic framework designed to cover a variety of arithmetic representations and their hardware implementations; both combinational and pipelined designs can be developed. Results show that, particularly for calculations involving large dynamic ranges, our tool can produce hardware that is smaller and faster when compared with a design adopting the reference representation.

Automating Customisation of Floating-Point Designs

Three-dimensional convolutional neural networks (3D CNNs) have demonstrated their outstanding classification accuracy for human action recognition (HAR). However, the large number of computations and parameters in 3D CNNs limits their deployability in real-life applications. To address this challenge, this paper adopts an algorithm-hardware co-design method by proposing an efficient 3D CNN building unit called 3D-1 bottleneck residual block (3D-1 BRB) at the algorithm level, and a corresponding FPGA-based hardware architecture called F-E3D at the hardware level. Based on 3D-1 BRB, a novel 3D CNN model called E3DNet is developed, which achieves nearly 37 times reduction in model size and 5% improvement in accuracy compared to standard 3D CNNs on the UCF101 dataset. Together with several hardware optimizations, including 3D fused BRB, online blocking and kernel reuse, the proposed F-E3D is nearly 13 times faster than a previous FPGA design for 3D CNNs, with performance and accuracy comparable to other state-of-the-art 3D CNN models on GPU platforms while requiring only 7% of their energy consumption.

/pdf/f-e3d-fpga-based-acceleration-of-an-efficient-3d-uyxuhj5bxi.pdf

F-E3D: FPGA-based Acceleration of an Efficient 3D Convolutional Neural Network for Human Action Recognition

Pipeline morphing is a simple but effective technique for reconfiguring pipelined FPGA designs at run time. By overlapping computation and reconfiguration, the latency associated with emptying and refilling a pipeline can be avoided. We show how morphing can be applied to linear and mesh pipelines at both word-level and bit-level, and explain how this method can be implemented using Xilinx 6200 FPGAs. We also present an approach using morphing to map a large virtual pipeline onto a small physical pipeline, and the trade-offs involved are discussed.

/pdf/pipeline-morphing-and-virtual-pipelines-4nzto44899.pdf

Pipeline morphing and virtual pipelines

Field-programmable gate array (FPGA) optimized random number generators (RNGs) are more resource-efficient than software-optimized RNGs because they can take advantage of bitwise operations and FPGA-specific features. However, it is difficult to concisely describe FPGA-optimized RNGs, so they are not commonly used in real-world designs. This paper describes a type of FPGA RNG called a LUT-SR RNG, which takes advantage of bitwise xor operations and the ability to turn lookup tables (LUTs) into shift registers of varying lengths. This provides a good resource-quality balance compared to previous FPGA-optimized generators, between the previous high-resource high-period LUT-FIFO RNGs and low-resource low-quality LUT-OPT RNGs, with quality comparable to the best software generators. The LUT-SR generators can also be expressed using a simple C++ algorithm contained within this paper, allowing 60 fully-specified LUT-SR RNGs with different characteristics to be embedded in this paper, backed up by an online set of very high speed integrated circuit hardware description language (VHDL) generators and test benches.

/pdf/the-lut-sr-family-of-uniform-random-number-generators-for-3wecg7jzfj.pdf

The LUT-SR Family of Uniform Random Number Generators for FPGA Architectures

Next generation DNA sequencing machines have been improving at an exceptional rate; the subsequent analysis of the generated sequenced data has become a bottleneck in current systems. This paper explores the use of reconfigurable hardware to accelerate the short read mapping problem, where the positions of millions of short DNA sequences are located relative to a known reference sequence. The proposed design comprises of an alignment processor based on a backtracking variation of the FM-index algorithm. The design represents a full solution to the short read mapping problem, capable of efficient exact and approximate alignment. We use reconfigurable hardware to accelerate the design and find that an implementation targeting the MaxWorkstation performs considerably faster and more energy efficient than current CPU and GPU based software aligners.

/pdf/hardware-acceleration-of-genetic-sequence-alignment-3wx18o6uw4.pdf

Wayne Luk

Papers

Automating Customisation of Floating-Point Designs

F-E3D: FPGA-based Acceleration of an Efficient 3D Convolutional Neural Network for Human Action Recognition

Pipeline morphing and virtual pipelines

The LUT-SR Family of Uniform Random Number Generators for FPGA Architectures

Hardware acceleration of genetic sequence alignment