FPGA technology mapping optimizaion by rewiring algorithms.

Tang Wai Chung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 40-41).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 2 --- Rewiring Algorithms --- p.3Chapter 2.1 --- REWIRE --- p.5Chapter 2.2 --- RAMFIRE --- p.7Chapter 2.3 --- GBAW --- p.8Chapter 3 --- FPGA Technology Mapping --- p.11Chapter 3.1 --- Problem Definition --- p.13Chapter 3.2 --- Network-flow-based Algorithms for FPGA Technology Mapping --- p.16Chapter 3.2.1 --- FlowMap --- p.16Chapter 3.2.2 --- FlowSYN --- p.21Chapter 3.2.3 --- CutMap --- p.22Chapter 4 --- LUT Minimization by Rewiring --- p.24Chapter 4.1 --- Greedy Decision Heuristic for LUT Minimization --- p.27Chapter 4.2 --- Experimental Result --- p.28Chapter 5 --- Conclusion --- p.38Bibliography --- p.4

Improving rewiring scheme and its applications on various circuit design problems.

Lo Wing Hang.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 60-61).Abstracts in English and Chinese.Abstract --- p.iChapter 1 --- Introduction --- p.1Chapter 2 --- Preliminaries --- p.5Chapter 2.1 --- Backgrounds and Definitions --- p.5Chapter 2.1.1 --- Boolean Network --- p.5Chapter 2.1.2 --- Transitive Fanin and Fanout Cone --- p.6Chapter 2.1.3 --- Controlling and Sensitizing Values --- p.6Chapter 2.1.4 --- Stuck-at Faults and Test Generation --- p.6Chapter 2.1.5 --- Mandatory Assignments --- p.8Chapter 2.2 --- Review of ATPG-based Rewiring --- p.9Chapter 3 --- Improved Single-Pass Rewiring Scheme Using Inconsistent Assignments --- p.14Chapter 3.1 --- Introduction --- p.14Chapter 3.2 --- Overview of FIRE --- p.15Chapter 3.3 --- Alternative Wire Identification Method --- p.17Chapter 3.3.1 --- Identifying Candidate Wires --- p.17Chapter 3.3.2 --- Redundancy Test on Candidate Wire --- p.18Chapter 3.4 --- Redundancy Identification Using Inconsistent Assignments --- p.21Chapter 3.5 --- Experimental Results --- p.26Chapter 3.6 --- Conclusions --- p.28Chapter 4 --- Improving Circuit Partitioning With Rewiring Techniques --- p.29Chapter 4.1 --- Introduction --- p.29Chapter 4.2 --- Implementation of Rewiring Schemes --- p.31Chapter 4.3 --- Coupling Partitioning Algorithm With Rewiring Techniques --- p.33Chapter 4.4 --- Experimental Results --- p.37Chapter 4.5 --- Conclusions --- p.43Chapter 5 --- Circuit Logic Level Reduction by Rewiring for FPGA Mapping --- p.45Chapter 5.1 --- Introduction --- p.45Chapter 5.2 --- Overview of the Technology Mapping Problem --- p.47Chapter 5.2.1 --- Problem Formulation --- p.47Chapter 5.2.2 --- FlowMap Algorithm Outline --- p.49Chapter 5.3 --- Logic Level Reduction by Rewiring Transformations --- p.51Chapter 5.4 --- Experimental Results --- p.54Chapter 5.5 --- Conclusions --- p.57Chapter 6 --- Conclusions and Future Works --- p.58Bibliography --- p.6

Logic perturbation based circuit partitioning and optimum FPGA switch-box designs.

Cheung Chak Chung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 101-114).Abstracts in English and Chinese.Abstract --- p.iAcknowledgments --- p.iiiVita --- p.vTable of Contents --- p.viList of Figures --- p.xList of Tables --- p.xivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Aims and Contribution --- p.4Chapter 1.3 --- Thesis Overview --- p.5Chapter 2 --- VLSI Design Cycle --- p.6Chapter 2.1 --- Logic Synthesis --- p.7Chapter 2.1.1 --- Logic Minimization --- p.8Chapter 2.1.2 --- Technology Mapping --- p.8Chapter 2.1.3 --- Testability --- p.8Chapter 2.2 --- Physical Design Synthesis --- p.8Chapter 2.2.1 --- Partitioning --- p.9Chapter 2.2.2 --- Floorplanning & Placement --- p.10Chapter 2.2.3 --- Routing --- p.11Chapter 2.2.4 --- "Compaction, Extraction & Verification" --- p.12Chapter 2.2.5 --- Physical Design of FPGAs --- p.12Chapter 3 --- Alternative Wiring --- p.13Chapter 3.1 --- Introduction --- p.13Chapter 3.2 --- Notation and Definitions --- p.15Chapter 3.3 --- Application of Rewiring --- p.17Chapter 3.3.1 --- Logic Optimization --- p.17Chapter 3.3.2 --- Timing Optimization --- p.17Chapter 3.3.3 --- Circuit Partitioning and Routing --- p.18Chapter 3.4 --- Logic Optimization Analysis --- p.19Chapter 3.4.1 --- Global Flow Optimization --- p.19Chapter 3.4.2 --- OBDD Representation --- p.20Chapter 3.4.3 --- Automatic Test Pattern Generation (ATPG) --- p.22Chapter 3.4.4 --- Graph Based Alternative Wiring (GBAW) --- p.23Chapter 3.5 --- Augmented GBAW --- p.26Chapter 3.6 --- Logic Optimization by using GBAW --- p.28Chapter 3.7 --- Conclusions --- p.31Chapter 4 --- Multi-way Partitioning using Rewiring Techniques --- p.33Chapter 4.1 --- Introduction --- p.33Chapter 4.2 --- Circuit Partitioning Algorithm Analysis --- p.38Chapter 4.2.1 --- The Kernighan-Lin (KL) Algorithm --- p.39Chapter 4.2.2 --- The Fiduccia-Mattheyses (FM) Algorithm --- p.42Chapter 4.2.3 --- Geometric Representation Algorithm --- p.46Chapter 4.2.4 --- The Multi-level Partitioning Algorithm --- p.49Chapter 4.2.5 --- Hypergraph METIS - hMETIS --- p.51Chapter 4.3 --- The GBAW Partitioning Algorithm --- p.53Chapter 4.4 --- Experimental Results --- p.56Chapter 4.5 --- Conclusions --- p.58Chapter 5 --- Optimum FPGA Switch-Box Designs - HUSB --- p.62Chapter 5.1 --- Introduction --- p.62Chapter 5.2 --- Background and Definitions --- p.65Chapter 5.2.1 --- Routing Architectures --- p.65Chapter 5.2.2 --- Global Routing --- p.67Chapter 5.2.3 --- Detailed Routing --- p.67Chapter 5.3 --- FPGA Router Comparison --- p.69Chapter 5.3.1 --- CGE --- p.69Chapter 5.3.2 --- SEGA --- p.70Chapter 5.3.3 --- TRACER --- p.71Chapter 5.3.4 --- VPR --- p.72Chapter 5.4 --- Switch Box Design --- p.73Chapter 5.4.1 --- Disjoint type switch box (XC4000-type) --- p.73Chapter 5.4.2 --- Anti-symmetric switch box --- p.74Chapter 5.4.3 --- Universal Switch box --- p.74Chapter 5.4.4 --- Switch box Analysis --- p.75Chapter 5.5 --- Terminology --- p.77Chapter 5.6 --- "Hyper-universal (4, W)-design analysis" --- p.82Chapter 5.6.1 --- "H3 is an optimum (4, 3)-design" --- p.84Chapter 5.6.2 --- "H4 is an optimum (4,4)-design" --- p.88Chapter 5.6.3 --- "Hi is a hyper-universal (4, i)-design for i = 5,6,7" --- p.90Chapter 5.7 --- Experimental Results --- p.92Chapter 5.8 --- Conclusions --- p.95Chapter 6 --- Conclusions --- p.99Chapter 6.1 --- Thesis Summary --- p.99Chapter 6.2 --- Future work --- p.100Chapter 6.2.1 --- Alternative Wiring --- p.100Chapter 6.2.2 --- Partitioning Quality --- p.100Chapter 6.2.3 --- Routing Devices Studies --- p.100Bibliography --- p.101Chapter A --- 5xpl - Berkeley Logic Interchange Format (BLIF) --- p.115Chapter B --- Proof of some 2-local patterns --- p.122Chapter C --- Illustrations of FM algorithm --- p.124Chapter D --- HUSB Structures --- p.127Chapter E --- Primitive minimal 4-way global routing Structures --- p.13

A Survey of Spiking Neural Network Accelerator on FPGA

Due to the ability to implement customized topology, FPGA is increasingly used to deploy SNNs in both embedded and high-performance applications. In this paper, we survey state-of-the-art SNN implementations and their applications on FPGA. We collect the recent widely-used spiking neuron models, network structures, and signal encoding formats, followed by the enumeration of related hardware design schemes for FPGA-based SNN implementations. Compared with the previous surveys, this manuscript enumerates the application instances that applied the above-mentioned technical schemes in recent research. Based on that, we discuss the actual acceleration potential of implementing SNN on FPGA. According to our above discussion, the upcoming trends are discussed in this paper and give a guideline for further advancement in related subjects

Ekstraksi Fitur Conflict of Interest pada Artikel Ilmiah Untuk Menentukan Kualitas Citation Author

Sitasi pada publikasi ilmiah mempengaruhi kualitas artikel sehingga akanberpengaruh terhadap kredibilitas author (peneliti). Terda pat banyak cara untuk meningkatkan kredibilitas peneliti, salah satunya adalah dengan melakukan sitasi terhadap diri sendiri (self citation). Namun, proses self citation yang berlebihan mengurangi kualitas sitasi paper tersebut. Terdapat banyak penelitian yang membuat metode untuk mengukur kualitas self-citation yang tidak sesuai, salah satunya dengan menggunakan rasio self-citation pada jendela waktu. Akan tetapi, metode ini tidak mempertimbangkan kesesuaian topik penelitian paper utama terhadap paper yang mensitasinya. Sehingga diperlukan adanya penentuan kualitas sitasi pada author agar dapat diketahui apakah peneliti sering meggunakan citation yang tidak sesuai topiknya berdasarkan paper author dan paper sitasi. Penelitian ini mengusulkan metode ekstraksi fitur conflict of interest untuk menentukan kualitas citation penulis artikel ilmiah. Hal ini dilakukan untuk mengetahui seberapa baik peneliti dalam menggunakan sitasinya. Terdapat 2 fitur yang diusulkan dalam penelitian ini. Pertama, fitur confict of interest yang didapatkan dari konflik kepentingan antara author paper dan author paper yang disitasi. Kedua, fitur similaritas konten yaitu fitur yang didapatkan dari kesamaan topik antar dokumen paper dan yang disitasinya. Metode similaritas yang digunakan adalah salah satu pendekatan deep learning yaitu Siamese Neural Network yang dikombinasikan dengan Long Short Term Memory. Kedua fitur ini selanjutnya diklasifikasi untuk menentukan kualitas citation author. Seluruh fitur akan diuji performanya pada proses klasifikasi. Hasil klasifikasi selanjutnya akan dihitung nilai akurasinya untuk mendapatkan performa fitur yang diusulkan. Hasil uji coba menunjukkan bahwa usulan fitur dapat digunakan untuk mengklasifikasi kualitas sitasi author. Hal ini ditunjukkan dengan nilai akurasi sebesar 66.67% pada klasifikasi Random Forest dan rata-rata akurasi sebesar 62% pada 3 klasifikasi yang digunakan. =================================================================================================== Citation on scientific paper affect on article quality so that it will affect on author credibility. There are many ways to increase the credibility of researchers, one of them is to do a self-citation. However, this process makes the calculation in bibliometric becoming less accurate because it doesn’t consider citation quality. There is some studies that proposed a method to measure an inappropriate self-citation, one of them is using self-citation ratio. But, this method doesnt consider topic relatedness between main paper and cited paper. So, its required to determine author’s citation quality to know that author are using anomalous citation based on main paper and each cited paper. This research proposed feature extraction conflict of interest to detect author’s citation quality. It allows us to know how right an author use citation in publication. Two features are proposed in this research. First, conflict of interest feature, is obtained from interest conflict between paper author and citation’s paper author. Second, content similarity feature, is obtained from the similarity between paper and cited papers of author. Deep learning approach is used to get the similarity of each document. Combination of Siamese neural network and Long Short-Term Memory can provide a better result on similarity based on training data. Last, all features will be combined with self-citation’s count feature based on previous research and classified to detect author’s citation quality. Features will be tested for its performance using classification. From the classification results, accuracy will be calculated to obtain the performance of the proposed feature. Based on the result, proposed feature can be used to classify author’s citation quality. It is shown with 66,67% of accuracy by using Random Forest classification and 62% of average accuracy on 3 classifier

Neuraghe: Exploiting CPU-FPGA synergies for efficient and flexible CNN inference acceleration on zynQ SoCs

Deep convolutional neural networks (CNNs) obtain outstanding results in tasks that require human-level understanding of data, like image or speech recognition. However, their computational load is significant, motivating the development of CNN-specialized accelerators. This work presents NEURAghe, a flexible and efficient hardware/software solution for the acceleration of CNNs on Zynq SoCs. NEURAghe leverages the synergistic usage of Zynq ARM cores and of a powerful and flexible Convolution-Specific Processor deployed on the reconfigurable logic. The Convolution-Specific Processor embeds both a convolution engine and a programmable soft core, releasing the ARM processors from most of the supervision duties and allowing the accelerator to be controlled by software at an ultra-fine granularity. This methodology opens the way for cooperative heterogeneous computing: While the accelerator takes care of the bulk of the CNN workload, the ARM cores can seamlessly execute hard-to-accelerate parts of the computational graph, taking advantage of the NEON vector engines to further speed up computation. Through the companion NeuDNN SW stack, NEURAghe supports end-to-end CNN-based classification with a peak performance of 169GOps/s and an energy efficiency of 17GOps/W. Thanks to our heterogeneous computing model, our platform improves upon the state-of-the-art, achieving a frame rate of 5.5 frames per second (fps) on the end-to-end execution of VGG-16 and 6.6fps on ResNet-18

Rewired retiming for flip-flop reduction and low power without delay penalty.

Jiang, Mingqi.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (leaves [49]-51).Abstract also in Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 2 --- Rewiring Background --- p.4Chapter 2.1 --- REWIRE --- p.6Chapter 2.2 --- GBAW --- p.7Chapter 3 --- Retiming --- p.9Chapter 3.1 --- Min-Clock Period Retiming --- p.9Chapter 3.2 --- Min-Area Retiming --- p.17Chapter 3.3 --- Retiming for Low Power --- p.18Chapter 3.4 --- Retiming with Interconnect Delay --- p.22Chapter 4 --- Rewired Retiming for Flip-flop Reduction --- p.26Chapter 4.1 --- Motivation and Problem Formulation --- p.26Chapter 4.2 --- Retiming Indication --- p.29Chapter 4.3 --- Target Wire Selection --- p.31Chapter 4.4 --- Incremental Placement Update --- p.33Chapter 4.5 --- Optimization Flow --- p.36Chapter 4.6 --- Experimental Results --- p.38Chapter 5 --- Power Analysis for Rewired Retiming --- p.41Chapter 5.1 --- Power Model --- p.41Chapter 5.2 --- Experimental Results --- p.44Chapter 6 --- Conclusion --- p.47Bibliography --- p.5

Logic Synthesis for Established and Emerging Computing

Logic synthesis is an enabling technology to realize integrated computing systems, and it entails solving computationally intractable problems through a plurality of heuristic techniques. A recent push toward further formalization of synthesis problems has shown to be very useful toward both attempting to solve some logic problems exactly--which is computationally possible for instances of limited size today--as well as creating new and more powerful heuristics based on problem decomposition. Moreover, technological advances including nanodevices, optical computing, and quantum and quantum cellular computing require new and specific synthesis flows to assess feasibility and scalability. This review highlights recent progress in logic synthesis and optimization, describing models, data structures, and algorithms, with specific emphasis on both design quality and emerging technologies. Example applications and results of novel techniques to established and emerging technologies are reported