8890864
Improving Error Tolerance for Multithreaded Register Files
Chip multithreaded computing is exposed to the dual challenges of increasing system complexity and error sensitivity. It is critical to develop effective solutions that achieve better error tolerance without inducing performance degradation. In this paper, we propose a new error-tolerant memory design based on a unique computing phenomenon referred to as the dynamic multithreading redundancy (DMR). The proposed technique exploits the interplay between the concurrent threads for runtime error control. We also present two DMR enhancements, immediate write-back and self-recovery, to address the error accumulation effect. A multithreaded register file was implemented to demonstrate the proposed DMR technique. Simulation results on the SPEC CPU2000 benchmarks demonstrate significant overhead reduction in performance and energy efficiency related to error recovery. In addition, the proposed technique features good scalability with respect to the instruction-level and thread-level parallelism for next-generation processor design, where the soft error problem is expected to get worse due to technology scaling and architecture-affecting trends.
7862909
A Study of Side-Channel Effects in Reliability-Enhancing Techniques
Reliability-enhancing techniques are critical for nanoscale integrated systems under the pressure of various physical non-idealities such as process variations and manufacturing defects. However, it is unclear how these techniques will affect the side-channel information leaked through hardware implementations. The related side-channel effects may have direct implications to the security requirement in a wide range of applications. In this paper, we investigate this new problem for trusted hardware design. Employing information-theoretic measures, the relationship between reliability enhancements and the induced side-channel effects is quantitatively evaluated. Simulation results on EDC/ECC schemes in memory circuits are presented to demonstrate the application of the proposed method.
16738561
Defect-tolerant digital filtering with unreliable molecular electronics
Molecular electronics such as silicon nanowires (NW) and carbon nanotubes (CNT) are considered to be the future computational substrates due to their ultra-high density and superior energy efficiency. However, excessive defects from bottom-up self-assembly fabrication pose a major technological barrier to achieving reliable computing at the molecular scale. Existing solutions targeting absolute correctness introduce high cost and complexity in post-fabrication testing and defect diagnosis. In this paper, we propose a new approach exploiting algorithm level enhancements for defect-insensitive signal processing. By deliberately allowing molecular-scale integrated systems to bear defects, the proposed design framework achieves reliable signal processing while significantly reduces the cost of defect tolerance.
14347347
Multilevel video representation with application to keyframe extraction
Content-based video analysis calls for efficient video representation. In this paper, a novel multi-level representation of video is proposed based on the principle components derived from low-level visual features. It can characterize the video content from the coarse level to the fine level according to its intrinsic structure. This representation form provides a flexible scheme for video content analysis such as summarization, classification, and retrieval. A newly proposed subspace method, kernel based PCA, is explored to achieve this conveniently. The application in keyframe extraction is investigated to demonstrate the benefits of this representation.
17864197
Low-complexity adaptive step size constrained constant modulus SG algorithms for adaptive beamforming
In this paper, we propose two low-complexity adaptive step size mechanisms to enhance the performance of stochastic gradient (SG) algorithms for adaptive beamforming. The beamformer is designed according to the constrained constant modulus (CCM) criterion and the proposed mechanisms are employed in the SG algorithm for implementation. A complexity comparison is provided to show their advantages over existing methods, and a sufficient condition for the convergence of the mean weight vector is established. Theoretical expressions of the excess mean-squared error (EMSE), in both the steady-state and tracking cases, are derived based on the energy conservation approach. The effects of multiple access interference (MAI) and additive noise are considered. Simulation experiments are presented for both the stationary and non-stationary scenarios, illustrating that the proposed algorithms achieve superior performance compared with existing methods, and verifying the accuracy of the analyses.
20762776
A Symmetric Orthogonal FastICA Algorithm and Applications in EEG
Extracting a number of independent components,FastICA algorithm can ensure that, each component extracted has never been extracted by adding orthogonalization steps. However, Gram-Schmidt orthogonal method implemented itself that estimation error of the first vector has been accumulated in the subsequent vectors. In this paper, the square root of the classical matrix method is used to achieve symmetric orthogonal that parallel to estimate the source of variables and avoid the Gram-Schmidt orthogonal method of estimation error accumulation problem. The symmetric orthogonal FastICA algorithm is apply in removal EEG artifacts (including eye movement, blinking, ECG, EMG, etc). The experiment and simulation results demonstrate that the symmetry orthogonal FastICA algorithm removes the artifacts and has a better separation efficiency and fast convergence.
12607701
An AIS-Based Service Match Model
Aiming at the problem that self-learning ability of the current system usually appears weak during service discovery, a novel artificial immune system (AIS) based service match model is proposed, which is based on the reference to the principles of cellpsilas mutation, evaluation and the quadratic response abilities, as well as simulation on the antibody-antigen identification mechanism. Theoretical analysis and simulations both show that the model can not only increasing the recall ratio and matching speed, but also realizing the function that deriving similar service request from known service request and then searching the best matching service. Furthermore, this service matching model is able to improve systempsilas adaptive learning, memorizing and dynamic evaluating abilities in service matching process.
29219228
Skew Detection Algorithm for Form Document Based on Elongate Feature
One new and efficient skew detection algorithm is proposed for form documents according to the feature that the horizontal line has the same skew with the form. This algorithm includes the following steps: Firstly, all horizontal connected regions, including horizontal straight-lines, are extracted from the form document by directional region growing method presented in this paper; Secondly, the optimal line is selected from all horizontal connected regions based on the elongate of connected region; Thirdly, all the pixels belonging to the optimal line are considered to calculate the line parameters with linear least-square theory. The skew angle of the optimal line is just the form skew angle. One elongate function is defined in this paper which described the elongate feature correctly for the band-like connected region. The experiment results show that the form skew angle can be detected accurately, and this skew detection algorithm is fast and robust.