Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

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[서평]「Computer Organization and Design, The Hardware/Software Interface」

Suggested by the structure of the visual nervous system, a new algorithm is proposed for pattern recognition. This algorithm can be realized with a multilayered network consisting of neuron-like cells. The network, “neocognitron”, is self-organized by unsupervised learning, and acquires the ability to recognize stimulus patterns according to the differences in their shapes: Any patterns which we human beings judge to be alike are also judged to be of the same category by the neocognitron. The neocognitron recognizes stimulus patterns correctly without being affected by shifts in position or even by considerable distortions in shape of the stimulus patterns.

Neocognitron--A New Algorithm for Pattern Recognition Tolerant of Deformations and Shifts in Position

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

Today's field programmable gate array (FPGA) architectures, like Xilinx's Virtex-II series, enable partial and dynamic run-time self-reconfiguration. This feature allows the substitution of parts of a hardware design implemented on this reconfigurable hardware, and therefore, a system can be adapted to the actual demands of applications running on the chip. Exploiting this possibility enables the development of adaptive hardware for a huge variety of applications. A novel method for communication interfaces using look up table (LUT)-based communication primitives enables an exact separation of reconfigurable parts and a fast and intelligent bus-system. A new adaptive software/hardware reconfigurable system is presented in this paper, using a real application in the automotive domain implemented on a Xilinx Virtex-II 3000 FPGA to present results

Dynamic and Partial FPGA Exploitation

Dynamic and partial reconfiguration (DPR) is a special feature offered by Xilinx Field Programmable Gate Arrays (FPGAs), giving the designer the ability to reconfigure a certain portion of the FPGA during run-time without influencing the other parts. This feature allows the hardware to be adaptable to any potential situation. For some applications, such as video-based driver assistance, the time needed to exchange a certain portion of the device might be critical. This paper addresses problems, limitations and results of on-chip reconfiguration that enable the user to decide whether DPR is suitable for a certain design prior to its implementation. A method is therefore introduced to calculate the expected reconfiguration throughput and latency. In addition, an IP core is presented that enables fast on-chip DPR close to the maximum achievable speed. Compared to an alternative state-of-the art realization, an increase in speed by a factor of 58 can be obtained.

A multi-platform controller allowing for maximum Dynamic Partial Reconfiguration throughput

This paper introduces a scalable hardware and software platform applicable for demonstrating the benefits of the invasive computing paradigm. The hardware architecture consists of a heterogeneous, tile-based manycore structure while the software architecture comprises a multi-agent management layer underpinned by distributed runtime and OS services. The necessity for invasive-specific hardware assist functions is analytically shown and their integration into the overall manycore environment is described.

Invasive manycore architectures

Current trends in high performance computing show, that the usage of multiprocessor systems on chip are one approach for the requirements of computing intensive applications. The multiprocessor system on chip (MPSoC) approaches often provide a static and homogeneous infrastructure of networked microprocessor on the chip die. A novel idea in this research area is to introduce the dynamic adaptivity of reconfigurable hardware in order to provide a flexible heterogeneous set of processing elements during run-time. This extension of the MPSoC idea by introducing run-time reconfiguration delivers a new degree of freedom for system design as well as for the optimized distribution of computing tasks to the adapted processing cells on the architecture related to the changing application requirements. The "computing in time and space"paradigm and the extension with the new degree of freedom for MPSoCs will be presented with the RAMPSoC approach described in this paper.

Runtime adaptive multi-processor system-on-chip: RAMPSoC

Dynamic and partial reconfiguration of Xilinx FPGAs is a well known technique in runtime adaptive system design. With this technique, parts of a configuration can be substituted while other parts stay operative without any disturbance. The advantage is the fact, that the spatial and temporal partitioning can be exploited with the goal to increase performance and to reduce power consumption due to the re-use of chip area. This paper shows a novel methodology for the inclusion of the configuration access port into the data path of a processor core in order to adapt the internal architecture and to re-use this access port as data- sink and source. It is obvious that the chip area, which is utilized by the hardware drivers for the internal configuration access port (ICAP), has to be as small as possible in comparison to the application functionality. Therefore, a hardware design with a small footprint, but with an adequate performance in terms of data throughput, is necessary. This paper presents a fast data path for dynamic and partial reconfiguration data with the advantage of a small footprint on the hardware resources.

Michael Hübner

Papers

Dynamic and Partial FPGA Exploitation

A multi-platform controller allowing for maximum Dynamic Partial Reconfiguration throughput

Invasive manycore architectures

Runtime adaptive multi-processor system-on-chip: RAMPSoC

Fast dynamic and partial reconfiguration data path with low hardware overhead on Xilinx FPGAs