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」

Due to its potential to greatly accelerate a wide variety of applications, reconfigurable computing has become a subject of a great deal of research. Its key feature is the ability to perform computations in hardware to increase performance, while retaining much of the flexibility of a software solution. In this survey, we explore the hardware aspects of reconfigurable computing machines, from single chip architectures to multi-chip systems, including internal structures and external coupling. We also focus on the software that targets these machines, such as compilation tools that map high-level algorithms directly to the reconfigurable substrate. Finally, we consider the issues involved in run-time reconfigurable systems, which reuse the configurable hardware during program execution.

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Reconfigurable computing: a survey of systems and software

Deep convolutional neural networks (CNNs) are widely used in modern AI systems for their superior accuracy but at the cost of high computational complexity. The complexity comes from the need to simultaneously process hundreds of filters and channels in the high-dimensional convolutions, which involve a significant amount of data movement. Although highly-parallel compute paradigms, such as SIMD/SIMT, effectively address the computation requirement to achieve high throughput, energy consumption still remains high as data movement can be more expensive than computation. Accordingly, finding a dataflow that supports parallel processing with minimal data movement cost is crucial to achieving energy-efficient CNN processing without compromising accuracy.In this paper, we present a novel dataflow, called row-stationary (RS), that minimizes data movement energy consumption on a spatial architecture. This is realized by exploiting local data reuse of filter weights and feature map pixels, i.e., activations, in the high-dimensional convolutions, and minimizing data movement of partial sum accumulations. Unlike dataflows used in existing designs, which only reduce certain types of data movement, the proposed RS dataflow can adapt to different CNN shape configurations and reduces all types of data movement through maximally utilizing the processing engine (PE) local storage, direct inter-PE communication and spatial parallelism. To evaluate the energy efficiency of the different dataflows, we propose an analysis framework that compares energy cost under the same hardware area and processing parallelism constraints. Experiments using the CNN configurations of AlexNet show that the proposed RS dataflow is more energy efficient than existing dataflows in both convolutional (1.4× to 2.5×) and fully-connected layers (at least 1.3× for batch size larger than 16). The RS dataflow has also been demonstrated on a fabricated chip, which verifies our energy analysis.

Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks

This paper introduces MorphoSys, a reconfigurable computing system developed to investigate the effectiveness of combining reconfigurable hardware with general-purpose processors for word-level, computation-intensive applications. MorphoSys is a coarse-grain, integrated, and reconfigurable system-on-chip, targeted at high-throughput and data-parallel applications. It is comprised of a reconfigurable array of processing cells, a modified RISC processor core, and an efficient memory interface unit. This paper describes the MorphoSys architecture, including the reconfigurable processor array, the control processor, and data and configuration memories. The suitability of MorphoSys for the target application domain is then illustrated with examples such as video compression, data encryption and target recognition. Performance evaluation of these applications indicates improvements of up to an order of magnitude (or more) on MorphoSys, in comparison with other systems.

MorphoSys: an integrated reconfigurable system for data-parallel and computation-intensive applications

The paper surveys a decade of R&D on coarse grain reconfigurable hardware and related CAD, points out why this emerging discipline is heading toward a dichotomy of computing science, and advocates the introduction of a new soft machine paradigm to replace CAD by compilation.

A decade of reconfigurable computing: a visionary retrospective

With the proliferation of highly specialized embedded computer systems has come a diversification of workloads for computing devices. General-purpose processors are struggling to efficiently meet these applications' disparate needs, and custom hardware is rarely feasible. According to the authors, reconfigurable computing, which combines the flexibility of general-purpose processors with the efficiency of custom hardware, can provide the alternative. PipeRench and its associated compiler comprise the authors' new architecture for reconfigurable computing. Combined with a traditional digital signal processor, microcontroller or general-purpose processor, PipeRench can support a system's various computing needs without requiring custom hardware. The authors describe the PipeRench architecture and how it solves some of the pre-existing problems with FPGA architectures, such as logic granularity, configuration time, forward compatibility, hard constraints and compilation time.

PipeRench: a reconfigurable architecture and compiler

Future computing workloads will emphasize an architecture's ability to perform relatively simple calculations on massive quantities of mixed-width data. This paper describes a novel reconfigurable fabric architecture, PipeRench, optimized to accelerate these types of computations. PipeRench enables fast, robust compilers, supports forward compatibility, and virtualizes configurations, thus removing the fixed size constraint present in other fabrics. For the first time we explore how the bit-width of processing elements affects performance and show how the PipeRench architecture has been optimized to balance the needs of the compiler against the realities of silicon. Finally, we demonstrate extreme performance speedup on certain computing kernels (up to 190x versus a modern RISC processor), and analyze how this acceleration translates to application speedup.

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PipeRench: a co/processor for streaming multimedia acceleration

PipeRench is a programmable datapath that can be used to accelerate numerically intensive applications. The unique aspect of PipeRench is its ability to virtualize hardware through self-managed dynamic reconfiguration. This capability provides application portability and scalability without redesign or recompilation. This paper describes the implementation of PipeRench in a 0.18 micron process. The implementation has 3.65 million transistors and runs at 120 MHz. Performance is competitive with high-end commercial DSP architectures and more than five times faster than a commercial microprocessor. Executing at 33 MHz, an FIR filter without virtualization consumes 519 mW. When virtualization is required, the implementation consumes approximately 675 mW.

PipeRench: A virtualized programmable datapath in 0.18 micron technology

While reconfigurable computing promises to deliver incomparable performance, it is still a marginal technology due to the high cost of developing and upgrading applications. Hardware virtualization can be used to significantly reduce both these costs. In this paper we describe the benefits of hardware virtualization, and show how it can be achieved using the technique of pipeline reconfiguration. The result is PipeRench, an architecture that supports robust compilation and provides forward compatibility. Our preliminary performance analysis on PipeRench predicts that it will outperform commercial FPGAs and DSPs in both overall performance and in performance normalized for silicon area over a broad range of problem sizes.

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Pipeline Reconfigurable FPGAs

The article defines a class of architectures for pipeline reconfigurable FPGAs by parameterizing a generic model. This class of architecture is sufficiently general to allow exploration of the most important design trade-offs. The parameters include the word size and LUT size, the number of global busses and registers associated with each logic block, and the horizontal interconnect within each stripe. We have developed an area model for the architecture that allows us to quickly estimate the area of an instance of the architectural class as a function of the parameter values. We compare the estimates generated by this model to one instance of the architecture that we have designed and fabricated.

M. Moe

Papers

PipeRench: a reconfigurable architecture and compiler

PipeRench: a co/processor for streaming multimedia acceleration

PipeRench: A virtualized programmable datapath in 0.18 micron technology

Pipeline Reconfigurable FPGAs

Characterization and parameterization of a pipeline reconfigurable FPGA