5,182 research outputs found

    Soft Computing Approach To Automatic Test Pattern Generation For Sequential Vlsi Circuit

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    Due to the constant development in the integrated circuits, the automatic test pattern generation problem become more vital for sequential vlsi circuits in these days. Also testing of integrating circuits and systems has become a difficult problem. In this paper we have discussed the problem of the automatic test sequence generation using particle swarm optimization(PSO) and technique for structure optimization of a deterministic test pattern generator using genetic algorithm(GA)

    Built-In Test Sequence Generation for Synchronous Sequential Circuits Based on Loading and Expansion of Test Subsequences

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    We describe an on-chip test generation scheme for synchronous sequential circuits that allows at-speed testing of such circuits. The proposed scheme is based on loading of (short) input sequences into an on-chip memory, and expansion of these sequences on-chip into test sequences. Complete coverage of modeled faults is achieved by basing the selection of the loaded sequences on a deterministic test sequence T 0, and ensuring that every fault detected by T 0 is detected by the expanded version of at least one loaded sequence. Experimental results presented for benchmark circuits show that the length of the sequence that needs to be stored at any time is on the average 10 % of the length of T 0, and that the total length of all the loaded sequences is on the average 46 % of the length of T 0. 1

    Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience.

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    Identifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be a salient feature of neural dynamics, but are not succinctly captured by traditional dimensionality reduction techniques. Here, we describe a software toolbox-called seqNMF-with new methods for extracting informative, non-redundant, sequences from high-dimensional neural data, testing the significance of these extracted patterns, and assessing the prevalence of sequential structure in data. We test these methods on simulated data under multiple noise conditions, and on several real neural and behavioral datas. In hippocampal data, seqNMF identifies neural sequences that match those calculated manually by reference to behavioral events. In songbird data, seqNMF discovers neural sequences in untutored birds that lack stereotyped songs. Thus, by identifying temporal structure directly from neural data, seqNMF enables dissection of complex neural circuits without relying on temporal references from stimuli or behavioral outputs

    New Techniques to Reduce the Execution Time of Functional Test Programs

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    The compaction of test programs for processor-based systems is of utmost practical importance: Software-Based Self-Test (SBST) is nowadays increasingly adopted, especially for in-field test of safety-critical applications, and both the size and the execution time of the test are critical parameters. However, while compacting the size of binary test sequences has been thoroughly studied over the years, the reduction of the execution time of test programs is still a rather unexplored area of research. This paper describes a family of algorithms able to automatically enhance an existing test program, reducing the time required to run it and, as a side effect, its size. The proposed solutions are based on instruction removal and restoration, which is shown to be computationally more efficient than instruction removal alone. Experimental results demonstrate the compaction capabilities, and allow analyzing computational costs and effectiveness of the different algorithms

    Static Compaction of Test Sequences for Synchronous Sequential Circuits

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    Today, VLSI design has progressed to a stage where it needs to incorporate methods of testing circuits. The Automatic Test Pattern Generation (ATPG) is a very attractive method and feasible on almost any combinational and sequential circuit. Currently available automatic test pattern generators (ATPGs) generate test sets that may be excessively long. Because a cost of testing depends on the test length. compaction techniques have been used to reduce that length. The motivation for studying test compaction is twofold. Firstly, by reducing the test sequence length. the memory requirements during the test application and the test application time are reduced. Secondly, the extent of test compaction possible for deterministic test sequences indicates that test pattern generators spend a significant amount of time generating test vectors that are not necessary. The compacted test sequences provide a target for more efficient deterministic test generators. Two types of compaction techniques exist: dynamic and static. The dynamic test sequence compaction performs compaction concurrently with the test generation process and often requires modification of the test generator. The static test sequence compaction is done in a post-processing step to the test generation and is independent of the test generation algorithm and process. In the thesis, a new idea for static compaction of test sequences for synchronous sequential circuits has been proposed. Our new method - SUSEM (Set Up Sequence Elimination Method) uses the circuit state information to eliminate some setup sequences for the target faults and consequently reduce the test sequence length. The technique has been used for the test sequences generated by HITEC test generator. ISCAS89 benchmark circuits were used in our experiments, for some circuits which have a large number of target faults and relatively small number of flip-flops, the very significant compactions have been obtained. The more important is that this method can be used to improve the test generation procedure unlike most static compaction methods which blindly or randomly remove parts of test vectors and cannot be used to improve the test generators

    On Real-Time AER 2-D Convolutions Hardware for Neuromorphic Spike-Based Cortical Processing

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    In this paper, a chip that performs real-time image convolutions with programmable kernels of arbitrary shape is presented. The chip is a first experimental prototype of reduced size to validate the implemented circuits and system level techniques. The convolution processing is based on the address–event-representation (AER) technique, which is a spike-based biologically inspired image and video representation technique that favors communication bandwidth for pixels with more information. As a first test prototype, a pixel array of 16x16 has been implemented with programmable kernel size of up to 16x16. The chip has been fabricated in a standard 0.35- m complimentary metal–oxide–semiconductor (CMOS) process. The technique also allows to process larger size images by assembling 2-D arrays of such chips. Pixel operation exploits low-power mixed analog–digital circuit techniques. Because of the low currents involved (down to nanoamperes or even picoamperes), an important amount of pixel area is devoted to mismatch calibration. The rest of the chip uses digital circuit techniques, both synchronous and asynchronous. The fabricated chip has been thoroughly tested, both at the pixel level and at the system level. Specific computer interfaces have been developed for generating AER streams from conventional computers and feeding them as inputs to the convolution chip, and for grabbing AER streams coming out of the convolution chip and storing and analyzing them on computers. Extensive experimental results are provided. At the end of this paper, we provide discussions and results on scaling up the approach for larger pixel arrays and multilayer cortical AER systems.Commission of the European Communities IST-2001-34124 (CAVIAR)Commission of the European Communities 216777 (NABAB)Ministerio de Educación y Ciencia TIC-2000-0406-P4Ministerio de Educación y Ciencia TIC-2003-08164-C03-01Ministerio de Educación y Ciencia TEC2006-11730-C03-01Junta de Andalucía TIC-141
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