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」

Efficient inference of Convolutional Neural Networks is a thriving topic recently. It is desirable to achieve the maximal test accuracy under given inference budget constraints when deploying a pre-trained model. Network pruning is a commonly used technique but it may produce irregular sparse models that can hardly gain actual speed-up. Group convolution is a promising pruning target due to its regular structure; however, incorporating such structure into the pruning procedure is challenging. It is because structural constraints are hard to describe and can make pruning intractable to solve. The need for configuring group convolution architecture, i.e., the number of groups, to maximise test accuracy also increases difficulty. This paper presents an efficient method to address this challenge. We formulate group convolution pruning as finding the optimal channel permutation to impose structural constraints and solve it efficiently by heuristics. We also apply local search to exploring group configuration based on estimated pruning cost to maximise test accuracy. Compared to prior work, results show that our method produces competitive group convolution models for various tasks within a shorter pruning period and enables rapid group configuration exploration subject to inference budget constraints.

/pdf/efficient-structured-pruning-and-architecture-searching-for-5f1yi7eq80.pdf

Efficient Structured Pruning and Architecture Searching for Group Convolution

On-FPGA communication is becoming more problematic as the long interconnection performance is deteriorating in technology scaling. In this paper, we address this issue by presenting a new wave-pipelined signaling scheme to achieve high-throughput communication in FPGA. The throughput and power consumption of a wave-pipelined link have been derived analytically and compared to the conventional synchronous link. Two circuit designs are proposed to realize wave-pipelined link using FPGA fabrics. The proposed approaches are also compared with conventional synchronous and asynchronous pipelining techniques. It is shown that, the wave-pipelined approach can achieve up to 5.66 times improvement in throughput versus the synchronous link and 13% improvement in power consumption and 35% improvement in delay versus the synchronous register-pipelining. Also, trade-offs between power, speed and area between the proposed and conventional designs are studied.

Wave-pipelined signaling for on-FPGA communication

Two systolic arrays for recursive digital filtering are presented. Both have a rectangular structure and produce output for a particular computation in consecutive cycles. Their performance is analyzed and the design tradeoffs are discussed. >

Systolic recursive filters

This paper presents a new model forglobal routings in FPGAs. The irregular interconnections in FPGAs can be generalized as multiple buffered interconnect stages, of which the electrical waveform can be adequately approximated. Based on the model, expressions of delay and fundamental throughput of the interconnections have been derived and validated. They are shown in line with the SPICE and FPGA experimental results. Moreover, the model shows that interconnection throughput can be significantly increased using wave-pipelined signaling instead of the conventional delay-based synchronous approach, as has been demonstrated in our FPGA experiments. We conclude this paper by having a discussion about a strategy to further enhance the interconnect throughput.

http://cas.ee.ic.ac.uk/people/stmak/Terrence_files/slip07c-mak.pdf

Global interconnections in FPGAs: modeling and performance analysis

This paper presents a novel approach for automatic optimisation of reconfigurable design parameters based on knowledge transfer. The key idea is to make use of insights derived from optimising related designs to benefit future optimisations. We show how to use designs targeting one device to speed up optimisation of another device. The proposed approach is evaluated based on various applications including computational finance and seismic imaging. It is capable of achieving up to 35% reduction in optimisation time in producing designs with similar performance, compared to alternative optimisation methods.

Wayne Luk

Papers

Efficient Structured Pruning and Architecture Searching for Group Convolution

Wave-pipelined signaling for on-FPGA communication

Systolic recursive filters

Global interconnections in FPGAs: modeling and performance analysis

Knowledge Transfer in Automatic Optimisation of Reconfigurable Designs