Hybrid Architecture Research Papers - Academia.edu (original) (raw)

In recent years, many systems have employed NAND flash memory as storage devices because of its advantages of higher performance (compared to the traditional hard disk drive), high-density, random-access, increasing capacity, and falling... more

In recent years, many systems have employed NAND flash memory as storage devices because of its advantages of higher performance (compared to the traditional hard disk drive), high-density, random-access, increasing capacity, and falling cost. On the other hand, the performance of NAND flash memory is limited by its ¿erase-before-write¿ requirement. Log-based structures have been used to alleviate this problem by writing updated data to the clean space. Prior log-based methods, however, cannot avoid excessive erase operations when there are frequent updates, which quickly consume free pages, especially when some data are updated repeatedly. In this paper, we propose a hybrid architecture for the NAND flash memory storage, of which the log region is implemented using phase change random access memory (PRAM). Compared to traditional log-based architectures, it has the following advantages: (1) the PRAM log region allows in-place updating so that it significantly improves the usage efficiency of log pages by eliminating out-of-date log records; (2) it greatly reduces the traffic of reading from the NAND flash memory storage since the size of logs loaded for the read operation is decreased; (3) the energy consumption of the storage system is reduced as the overhead of writing and reading log data is decreased with the PRAM log region; (4) the lifetime of NAND flash memory is increased because the number of erase operations are reduced. To facilitate the PRAM log region, we propose several management policies. The simulation results show that our proposed methods can substantially improve the performance, energy consumption, and lifetime of the NAND flash memory storage1.

One of the main obstacles in applying genetic algorithms (GA's) to complex problems has been the high computational cost due to their slow convergence rate. We encountered such a difficulty in our attempt to use the classical GA for... more

One of the main obstacles in applying genetic algorithms (GA's) to complex problems has been the high computational cost due to their slow convergence rate. We encountered such a difficulty in our attempt to use the classical GA for estimating parameters of a metabolic model. To alleviate this difficulty, we developed a hybrid approach that combines a GA with a stochastic variant of the simplex method in function optimization. Our motivation for developing the stochastic simplex method is to introduce a cost-effective exploration component into the conventional simplex method. In an attempt to make effective use of the simplex operation in a hybrid GA framework, we used an elite-based hybrid architecture that applies one simplex step to a top portion of the ranked population. We compared our approach with five alternative optimization techniques including a simplex-GA hybrid independently developed by Renders-Bersini (R-B) and adaptive simulated annealing (ASA). Our empirical evaluations showed that our hybrid approach for the metabolic modeling problem outperformed all other techniques in terms of accuracy and convergence rate. We used two additional function optimization problems to compare our approach with the five alternative methods

In this paper we present a sound-source model for localising and tracking an acoustic source of interest along the azimuth plane in acoustically cluttered environments, for a mobile service robot. The model we present is a hybrid... more

In this paper we present a sound-source model for localising and tracking an acoustic source of interest
along the azimuth plane in acoustically cluttered environments, for a mobile service robot. The model
we present is a hybrid architecture using cross-correlation and recurrent neural networks to develop a
robotic model accurate and robust enough to perform within an acoustically cluttered environment. This
model has been developed with considerations of both processing power and physical robot size, allowing
for this model to be deployed on to a wide variety of robotic systems where power consumption and
size is a limitation. The development of the system we present has its inspiration taken from the central
auditory system (CAS) of the mammalian brain. In this paper we describe experimental results of the
proposed model including the experimental methodology for testing sound-source localisation systems.
The results of the system are shown in both restricted test environments and in real-world conditions.
This paper shows how a hybrid architecture using band pass filtering, cross-correlation and recurrent
neural networks can be used to develop a robust, accurate and fast sound-source localisation model for a
mobile robot.

This paper presents a general methodology for the specification and the integration of functional modules in a distributed reactive robot architecture. The approach is based on a hybrid architecture basically composed of two levels: a... more

This paper presents a general methodology for the specification and the integration of functional modules in a distributed reactive robot architecture. The approach is based on a hybrid architecture basically composed of two levels: a lower distributed functional level controlled by a centralized decisional level. Due to this methodology, synchronous or asynchronous operating capabilities (servo-control, data processing, event monitoring) can

This paper presents a novel hybrid encoding method for encoding of low-density parity-check (LDPC) codes. The design approach is applied to design 10-Gigabit Ethernet transceivers over copper cables. For a specified encoding speed, the... more

This paper presents a novel hybrid encoding method for encoding of low-density parity-check (LDPC) codes. The design approach is applied to design 10-Gigabit Ethernet transceivers over copper cables. For a specified encoding speed, the proposed method requires substantially lower complexity in terms of area and storage. Furthermore, this method is generic and can be adapted easily for other LDPC codes. One major advantage of this design is that it does not require column swapping and it maintains compatibility with optimized LDPC decoders. For a 10-Gigabit Ethernet transceiver which is compliant with the IEEE 802.3 an standard, the proposed sequential (5-Parallel) hybrid architecture has the following implementation properties: critical path: (log2(324) + 1)Txor + Tand, number of XOR gates: 11 056, number of and gates: 1620, and ROM storage: 104 976 bits (which can be minimized to 52 488 bits using additional hardware). This method achieves comparable critical path, and requires 74%...

We propose a hybrid architecture for the NTCIR-5 CLQA C-C (Cross Language Question Answering from Chinese to Chinese) Task. Our system, the Academia Sinica Question-Answering System (ASQA), outputs exact answers to six types of factoid... more

We propose a hybrid architecture for the NTCIR-5 CLQA C-C (Cross Language Question Answering from Chinese to Chinese) Task. Our system, the Academia Sinica Question-Answering System (ASQA), outputs exact answers to six types of factoid question: personal names, location names, organization names, artifacts, times, and numbers. The architecture of ASQA comprises four main components: Question Processing, Passage Retrieval, Answer Extraction, and Answer Ranking. ASQA successfully combines machine learning and knowledge-based approaches to answer Chinese factoid questions, achieving 37.5% and 44.5% Top1 accuracy for correct, and correct+unsupported answers, respectively.

Sparse iterative linear solvers are critical for large-scale scientific simulations, many of which spend the majority of their run time in solvers. Algebraic Multigrid (AMG) is a popular solver because of its linear run-time complexity... more

Sparse iterative linear solvers are critical for large-scale scientific simulations, many of which spend the majority of their run time in solvers. Algebraic Multigrid (AMG) is a popular solver because of its linear run-time complexity and its proven scalabil-ity in distributed-memory ...