904578
Simulation-based test algorithm generation for random access memories
Although there are well known test algorithms that have been used by the industry for years for testing semiconductor random-access memories (RAMs), systematic evaluation of their effectiveness and efficiency has been a difficult job. In the past, it was mainly done manually by proving a certain algorithm can detect a certain type of fault. As memory technology keeps innovating, the growing complexity of the memories and number of fault types that need to be covered will require more effective and efficient test algorithms to be discovered in much shorter time. A systematic approach for developing and evaluating memory test algorithms is thus desired. We propose such an approach here: test algorithm generation by simulation (TAGS), which generates and optimizes test algorithms, given a test time budget. Experimental results show that the algorithms generated by TAGS are more efficient than the traditional test algorithms. Using TAGS, a series of test algorithms with a detailed list of faults covered by each algorithm can be generated, providing easy trade-off between test time and fault coverage.
6577464
An HMAC processor with integrated SHA-1 and MD5 algorithms
Cryptographic algorithms are prevalent and important in digital communications and storage, e.g., both SHA-1 and MD5 algorithms are widely used hash functions in IPSec and SSL for checking the data integrity. In this paper, we propose a hardware architecture for the standard HMAC function that supports both. Our HMAC design automatically generates the padding words and reuses the key for consecutive HMAC jobs that use the same key. We have also implemented the HMAC design in silicon. Compared with existing designs, our HMAC processor has lower hardware cost---12.5% by sharing of the SHA-1 and MD5 circuitry and a little performance penalty.
19145806
An adaptive architecture for multi-stream authoring and presentations in distributed networks
New global trends in communications technology and liberalization are forcing dramatic changes in our lives. Applications tend to be lightweight and mobile, resulting in greater convenience. We present a new architecture to advance the traditional authoring system towards these trends. In the proposed architecture, the authoring system is capable of editing media content distributed in the networks. It forms a new model of authoring, i.e., the distributed authoring model. The distributed authoring model is described and compared with the traditional authoring model. We introduce quality-on-demand streaming in the architecture for the purpose of distributed authoring. Furthermore, we address the problems of applying multiple source streaming and quality adaptation in the architecture and show our design for resolving these problems. An implementation conforming to this architecture is also described.
2224019
Using an Immune System Model to Explore Mate Selection in Genetic Algorithms
When Genetic Algorithms (GAs) are employed in multimodal function optimization, engineering and machine learning, identifying multiple peaks and maintaining subpopulations of the search space are two central themes. In this paper, an immune system model is adopted to develop a framework for exploring the role of mate selection in GAs with respect to these two issues. The experimental results reported in the paper will shed more light into how mate selection schemes compare to traditional selection schemes. In particular, we show that dissimilar mating is beneficial in identifying multiple peaks, yet harmful in maintaining subpopulations of the search space.
8079480
A Systematic Study of Genetic Algorithms with Genotype Editing
This paper continues our systematic study of an RNA- editing computational model of Genetic Algorithms (GA). This model is constructed based on several genetic editing characteristics that are gle- aned from the RNA editing system as observed in several organisms. We have expanded the traditional Genetic Algorithm with artificial editing mechanisms as proposed in (11) and (12). The incorporation of editing mechanisms, which stochastically alter the information encoded in the genotype, provides a means for artificial agents with genetic descriptions to gain greater phenotypic plasticity, which may be environmentally re- gulated. The systematic study of this artificial genotype editing model has shed some light into the evolutionary implications of RNA editing and how to select proper genotype editors to design more robust GAs. Our results also show promising applications to complex real-world pro- blems. We expect that the framework here developed will both facilitate determining the evolutionary role of RNA editing in biology, and advance the current state of research in Evolutionary Computation.
5119687
Adaptive MPI
Processor virtualization is a powerful technique that enables the runtime system to carry out intelligent adaptive optimizations like dynamic resource management. Charm++ is an early language/system that supports processor virtualization. This paper describes Adaptive MPI or AMPI, an MPI implementation and extension, that supports processor virtualization. AMPI implements virtual MPI processes (VPs), several of which may be mapped to a single physical processor. AMPI includes a powerful runtime support system that takes advantage of the degree of freedom afforded by allowing it to assign VPs onto processors. With this runtime system, AMPI supports such features as automatic adaptive overlap of communication and computation and automatic load balancing. It can also support other features such as checkpointing without additional user code, and the ability to shrink and expand the set of processors used by a job at runtime. This paper describes AMPI, its features, benchmarks that illustrate performance advantages and tradeoffs offered by AMPI, and application experiences.
9984897
Structure Alignment Using Bilingual Chunking
A new statistical method called "bilingual chunking" for structure alignment is proposed. Different with the existing approaches which align hierarchical structures like sub-trees, our method conducts alignment on chunks. The alignment is finished through a simultaneous bilingual chunking algorithm. Using the constrains of chunk correspondence between source language (SL) and target language (TL), our algorithm can dramatically reduce search space, support time synchronous DP algorithm, and lead to highly consistent chunking. Furthermore, by unifying the POS tagging and chunking in the search process, our algorithm alleviates effectively the influence of POS tagging deficiency to the chunking result.The experimental results with English-Chinese structure alignment show that our model can produce 90% in precision for chunking, and 87% in precision for chunk alignment.