This course covers advanced topics in design concepts and implementation principles of application specific processors, including embedded system design principles and application specific processors, application specific instruction set processors, domain specific processor architectures, embedded memories and dynamically customizable processors. CSE 240A recommended.
Score: 5.4045453 Details | Listing | Web page
VLSI integrated-circuit building blocks of computing systems, and their implementation. Computer-aided design and performance simulations, design exercises and projects. Devices, standard cells and interconnects, clocking, power/ground distribution, arithmetic modules, memories. Methodologies and tradeoffs in system implementation.
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Couplings among timing, circuits and spatial embedding in nanometer-scale CMOS design. The role, and key problems, of physical layout in IC implementation. Example topics: RTL-to-GDSII methodologies, analyses and estimations, partitioning, floor planning, placement, routing, special net routing, cell generation, compaction.
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Hardware software co-design, architectural level synthesis, control synthesis and optimization, scheduling, binding, register and bus sharing, interconnect design, module selection, combinational logic optimization, state minimization, state encoding, and retiming.
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Design for test, testing economics, defects, failures and faults, fault models, fault simulation, automatic test pattern generation, functional testing, memory, PLA, FPGA, microprocessor test, and fault diagnosis.
Score: 5.4045453 Details | Listing | Web page
Scan path design, BIST architectures, test point insertion, self-checking circuits, test and fault tolerance in architectural synthesis, reconfigurable fault tolerant hardware, and SOC test design.
Score: 5.4045453 Details | Listing | Web page
This course is about the computer algorithms, techniques, and theory used in the simulation and verification of electrical circuits.
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Number representation, fixed point adders, subtracters, and multipliers; modified booth’s recoding, high-radix multiplication, (non)restoring dividers, SRT division, high-radix dividers, division by convergence, square-rooting, floating point arithmetic, rounding schemes, errors and error control, and floating point adders, subtracters, multipliers, dividers.
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This course covers architecture concepts used to tailor processors to a specific application or sets of applications. It covers Field-Programmable Gate Arrays (FPGAs), various forms of Application Specific Integrated Circuit (ASIC) designs, Application Specific Integrated Processors (ASIP), and augmenting customizable VHDL cores.
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Algorithmic techniques and optimization frameworks for large-scale, difficult optimizations. Primal-dual multicommodity flow approximations, approximations for geometric and graph Steiner formulations, continuous placement optimization, heuristics for Boolean satisfiability, multilevel methods, semidefinite programming, and application to other formulations (e.g., scheduling).
Score: 5.4045453 Details | Listing | Web page
Topics of special interest in computer architecture to be presented by faculty and students under faculty direction. Topics vary from quarter to quarter. May be repeated for credit.
Score: 5.4045453 Details | Listing | Web page
Topics of special interest in VLSI to be presented by faculty and students under faculty direction. Topics vary from quarter to quarter. May be repeated for credit.
Score: 5.4045453 Details | Listing | Web page
Topics of special interest in CAD to be presented by faculty and students under faculty direction. Topics vary from quarter to quarter. May be repeated for credit.
Score: 5.4045453 Details | Listing | Web page
Heuristic search algorithms including A*, constraint satisfaction algorithms including DPLL, randomized search, knowledge representation in first-order logic (FOL), resolution methods for reasoning in FOL, reasoning about action and planning, reasoning with Bayesian networks. CSE 101 recommended.
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Classifier learning including linear separators, decision trees, and nearest neighbors. Generalization and overfitting; design of learning experiments; the PAC model. Possible topics include ensemble methods, boosting, kernel methods, online learning, and reinforcement learning.
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Comprehensive introduction to computer vision providing broad coverage including low-level vision (image formation, photometry, color, image feature detection), inferring 3-D properties from images (shape-from shading, stereo vision, motion interpretation) and object recognition. Companion to CSE 252B covering complementary topics.
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Comprehensive introduction to computer vision providing focused coverage of multiview geometry, structure from motion, image segmentation, motion segmentation, texture analysis and recognition, object detection, and image-based rendering. Companion to CSE 252A covering complementary topics.
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Selected topics in computer vision and statistical pattern recognition, with an emphasis on recent developments. Possible topics include: grouping and segmentation, object recognition and tracking, multiple view geometry, kernel-based methods, dimensionality reduction, and mixture models.
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Probability density estimation, perceptrons, multilayer neural networks, radial basis function networks, support vector machines, error functions, data preprocessing. Possible topics include unsupervised learning methods, recurrent networks, and mathematical learning theory. CSE 250B recommended.
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Learning algorithms based on statistics. Possible topics include minimum-variance unbiased estimators, maximum likelihood estimation, likelihood ratio tests, resampling methods, linear logistic regression, feature selection, regularization, dimensionality reduction, manifold detection. An upper-division undergraduate course on probability and statistics such as Math. 183 or 186, or any graduate course on statistics, pattern recognition, or machine learning is recommended.
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Introduction to modern statistical approaches to natural language processing: part-of-speech tagging, word-sense disambiguation and parsing, using Markov models, hidden Markov models and probabilistic context-free grammars. Recommended
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Computational methods are indispensable to an understanding of the vast datasets emerging from human and other organisms’ genomes. This course surveys algorithms underlying genome analysis, sequence alignment, phylogenetic trees, protein folding, gene expression, metabolic pathways, and biological knowledge base design.
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Renumbered to CSE 206B (see above).
Score: 5.4045453 Details | Listing | Web page
Connectionist models and a sampling of other cognitive modeling techniques. Models of language processing, memory, sequential processes, and vision. Areas covered may vary depending on student and faculty interests. Can be repeated for credit. CSE 151 or CSE 250B or CSE 253 or CSE 254, or equivalent experience recommended.
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A weekly meeting featuring local (and occasional external) speakers discussing their current research in Artificial Intelligence Neural Networks, and Genetic Algorithms. (S/U grades only.)
Score: 5.4045453 Details | Listing | Web page
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