Conventional L2 Research Articles

Design of Hybrid Second-Level Caches

In recent years, embedded dynamic random-access memory (eDRAM) technology has been implemented in last-level caches due to its low leakage energy consumption and high density. However, the fact that eDRAM presents slower access time than static RAM (SRAM) technology has prevented its inclusion in higher levels of the cache hierarchy. This paper proposes to mingle SRAM and eDRAM banks within the data array of second-level (L2) caches. The main goal is to achieve the best trade-off among performance, energy, and area. To this end, two main directions have been followed. First, this paper explores the optimal percentage of banks for each technology. Second, the cache controller is redesigned to deal with performance and energy. Performance is addressed by keeping the most likely accessed blocks in fast SRAM banks. In addition, energy savings are further enhanced by avoiding unnecessary destructive reads of eDRAM blocks. Experimental results show that, compared to a conventional SRAM L2 cache, a hybrid approach requiring similar or even lower area speedups the performance on average by 5.9 percent, while the total energy savings are by 32 percent. For a 45 nm technology node, the energy-delay-area product confirms that a hybrid cache is a better design than the conventional SRAM cache regardless of the number of eDRAM banks, and also better than a conventional eDRAM cache when the number of SRAM banks is an eighth of the total number of cache banks.

Using the L* concept to explore controls on the relationship between paired ligand and dissolved iron concentrations in the ocean

Ligand (L) dynamics are inextricably linked to iron biogeochemistry, and their binding characteristics define much of the oceanic distributions of dissolved iron (DFe). Usually, L concentrations [L] are considered to be perennially in excess of [DFe] at any oceanic locale or point in time. Here we use the biogeochemical * concept to investigate whether distinct trends and patterns are evident for L* (the excess of [L] over [DFe]) across the two conventional ligand classes L1 and L2. The largest global datasets are available for L2* and point overwhelmingly to positive L2* values (but clearly establishing whether ligands in published studies are L2 versus L1 can be problematic). This trend is also apparent, for a more limited dataset, for L1*. Negative L2* values are mainly linked to high-iron waters (>2nmolL−1). Datasets from process studies, such as mesoscale iron-enrichments and shipboard particle remineralisation time-series, provide insights into the main drivers of L* in surface and subsurface waters, respectively. Multiple studies reveal rapid (days) microbial responses to iron-enrichment, with L1* increasing from negative to positive values. Deeper in the water column, particle remineralisation releases L2 concurrently with DFe but at higher concentrations (i.e. +L2*). We propose that +L1* is driven by opportunism within marine bacteria, but the magnitude of L1* is constrained by the energetic demands of producing siderophores, for example in response to episodic iron-enrichment, such that L1 is produced in slight excess only. In contrast, during subsurface particle solubilisation, +L2* values are probably driven by concurrent release of a larger excess of organic compounds (linked to major elements like C, which can act as L2) relative to trace amounts of DFe.

Complex-valued sparse reconstruction via arctangent regularization

Complex-valued sparse reconstruction is conventionally solved by transforming it into real-valued problems. However, this method might not work efficiently and correctly, especially when the size of the problem is large, or the mutual coherence is high. In this paper, we present a novel algorithm called the arctangent regularization (ATANR), which can handle the complex-valued problems of large size and high mutual coherence directly. The ATANR is implemented with the iterative least squares (IRLS) framework, and accelerated by the dimension reduction and active set selection steps. Further, we summarize and analyze the common properties of a penalty kernel which is suitable for sparse reconstruction. The analyses show that the key difference, between the arctangent kernel and the ℓ1 norm, is that the first order derivative of ATANR is close to zero for a nonzero variable. This will make ATANR less sensitive to the regularization parameter λ than ℓ1 regularization methods. Finally, lots of numerical experiments validate that ATANR usually has better performance than the conventional ℓ1 regularization methods, not only for the random signs ensemble, but also for the sensing matrix with high mutual coherence, such as the resolution enhancement case.

A robust compressive sensing based technique for reconstruction of sparse radar scenes

Pulse-Doppler radar has been successfully applied to surveillance and tracking of both moving and stationary targets. For efficient processing of radar returns, delay–Doppler plane is discretized and FFT techniques are employed to compute matched filter output on this discrete grid. However, for targets whose delay–Doppler values do not coincide with the computation grid, the detection performance degrades considerably. Especially for detecting strong and closely spaced targets this causes miss detections and false alarms. This phenomena is known as the off-grid problem. Although compressive sensing based techniques provide sparse and high resolution results at sub-Nyquist sampling rates, straightforward application of these techniques is significantly more sensitive to the off-grid problem. Here a novel parameter perturbation based sparse reconstruction technique is proposed for robust delay–Doppler radar processing even under the off-grid case. Although the perturbation idea is general and can be implemented in association with other greedy techniques, presently it is used within an orthogonal matching pursuit (OMP) framework. In the proposed technique, the selected dictionary parameters are perturbed towards directions to decrease the orthogonal residual norm. The obtained results show that accurate and sparse reconstructions can be obtained for off-grid multi target cases. A new performance metric based on Kullback–Leibler Divergence (KLD) is proposed to better characterize the error between actual and reconstructed parameter spaces. Increased performance with lower reconstruction errors are obtained for all the tested performance criteria for the proposed technique compared to conventional OMP and ℓ1 minimization techniques.

Convex relaxation based sparse algorithm for hyperspectral target detection

Target detection in hyperspectral images is an important task. In this paper, we propose a sparsity based algorithm for target detection in hyperspectral images. In sparsity model, each hyperspectral pixel is represented by a linear combination of a few samples from an overcomplete dictionary, and the weighted vector for such reconstruction is sparse. This model has been applied in hyperspectral target detection and solved with several greedy algorithms. As conventional greedy algorithms may be trapped into a local optimum, we consider an alternative way to regularize the model and find a more accurate solution to the model. The proposed method is based on convex relaxation technique. The original sparse representation problem is regularized with a properly designed weighted ℓ1 minimization and effectively solved with existing solver. The experiments on synthetic and real hyperspectral data suggest that the proposed algorithm outperforms the classical sparsity-based detection algorithms, such as Simultaneous Orthogonal Matching Pursuit (SOMP) and Simultaneous Subspace Pursuit (SSP) and conventional ℓ1 minimization.

An iterative thresholding algorithm for the inverse problem of electrical resistance tomography

Image reconstruction in Electrical Resistance Tomography (ERT) is an ill-posed nonlinear inverse problem. Considering the sparsity property of ERT model, in this paper, we replace the conventional l2 regularization penalty term by weighted lp(1≤p<2) penalty term. To overcome the non-quadratic property, a surrogate term is added to the objective function. An interesting condition is that the classical methods (e.g. SVD, Landweber iteration) can be used to solve the lp(1≤p<2) least squares problems. Both typical and complicated distributions (e.g. annular and cross-shape) have been examined using a 16-electrode configuration based on the finite element method (FEM) software COMSOL. The simulated results demonstrate the feasibility of the proposed algorithm, and compared to the l2 regularization method, the proposed algorithms can produce images of higher quality, which are evaluated both qualitatively and quantitatively.

Smoothly Clipped Absolute Deviation (SCAD) regularization for compressed sensing MRI Using an augmented Lagrangian scheme

PurposeCompressed sensing (CS) provides a promising framework for MR image reconstruction from highly undersampled data, thus reducing data acquisition time. In this context, sparsity-promoting regularization techniques exploit the prior knowledge that MR images are sparse or compressible in a given transform domain. In this work, a new regularization technique was introduced by iterative linearization of the non-convex smoothly clipped absolute deviation (SCAD) norm with the aim of reducing the sampling rate even lower than it is required by the conventional l1 norm while approaching an l0 norm. Materials and MethodsThe CS-MR image reconstruction was formulated as an equality-constrained optimization problem using a variable splitting technique and solved using an augmented Lagrangian (AL) method developed to accelerate the optimization of constrained problems. The performance of the resulting SCAD-based algorithm was evaluated for discrete gradients and wavelet sparsifying transforms and compared with its l1-based counterpart using phantom and clinical studies. The k-spaces of the datasets were retrospectively undersampled using different sampling trajectories. In the AL framework, the CS-MRI problem was decomposed into two simpler sub-problems, wherein the linearization of the SCAD norm resulted in an adaptively weighted soft thresholding rule with a sparsity enhancing effect. ResultsIt was demonstrated that the proposed regularization technique adaptively assigns lower weights on the thresholding of gradient fields and wavelet coefficients, and as such, is more efficient in reducing aliasing artifacts arising from k-space undersampling, when compared to its l1-based counterpart. ConclusionThe SCAD regularization improves the performance of l1-based regularization technique, especially at reduced sampling rates, and thus might be a good candidate for some applications in CS-MRI.

Dual-Band Miniature Coupled Double Loop GPS Antenna Loaded With Lumped Capacitors and Inductive Pins

A miniature coupled double loop (CDL) antenna for dual-band GPS application is presented. The antenna layout employs lumped capacitors and inductive pins over a moderately high permittivity Rogers TMM10i (ε <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> = 9.8) substrate to concurrently achieve the goals of size miniaturization, high radiation efficiency, and proper spacing of L2 and L1 band resonance frequencies. For performance verification, an antenna prototype has been fabricated over a 500 mil thick substrate with an overall L2 band size of λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> /8.8×λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> /8.8×λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> /19.4. The antenna operates with > 50% less area as compared to a conventional L2 band patch realized over the same substrate. The measured broadside RHCP realized gains are 4.9 and 3.1 dB over a 24"×24" ground plane with 26 and 35 MHz 3 dB realized gain bandwidths at the L2 and L1 bands, respectively. The antenna operates with 82% L2 and 72% L1 band measured radiation efficiencies.

Versatile stream buffer architecture to exploit the high memory bandwidth of 3-D IC technology

Three-dimensional (3-D) integration technology provides various architectural opportunities including huge memory bandwidth. This paper proposes versatile stream buffer architecture to work as a secondary victim cache as well as the conventional stream buffer. The versatile stream buffer utilizes empty spaces to exploit massive memory bandwidth provided by 3-D integration technology and to reduce memory access frequency. Performance evaluation results show that the proposed mechanism with a 16KB stream buffer and a 4KB victim cache can achieve better performance than the conventional L2 cache with the capacity of 256KB and 2MB by 10% and 3%, respectively. The proposed mechanism reduces the miss rate by about 12% more than the conventional L2 cache with the capacity of 256KB.

Linear classifier combination and selection using group sparse regularization and hinge loss

The main principle of stacked generalization is using a second-level generalizer to combine the outputs of base classifiers in an ensemble. In this paper, after presenting a short survey of the literature on stacked generalization, we propose to use regularized empirical risk minimization (RERM) as a framework for learning the weights of the combiner which generalizes earlier proposals and enables improved learning methods. Our main contribution is using group sparsity for regularization to facilitate classifier selection. In addition, we propose and analyze using the hinge loss instead of the conventional least squares loss. We performed experiments on three different ensemble setups with differing diversities on 13 real-world datasets of various applications. Results show the power of group sparse regularization over the conventional l1 norm regularization. We are able to reduce the number of selected classifiers of the diverse ensemble without sacrificing accuracy. With the non-diverse ensembles, we even gain accuracy on average by using group sparse regularization. In addition, we show that the hinge loss outperforms the least squares loss which was used in previous studies of stacked generalization.

A geometric mixed norm approach to shallow water acoustic channel estimation and tracking

The shallow water acoustic channel is challenging to estimate and track due to rapid temporal fluctuations of its large delay spread. However, the impulse response and representations of its time-variability often exhibit a sparse structure that can be exploited to improve estimator performance. We propose a sparse reconstruction of the shallow water acoustic channel that employs a novel optimization metric combining the complex square root of the channel coefficients and a non-convex complex function based on the L2 estimation error. Our mixed norm formulation is mathematically equivalent to conventional L2 constrained L1 minimization, but fundamentally different in the non-convex topology we employ to solve for and track the optimal coefficients in real time directly over the complex field. Our estimation and tracking algorithm is designed for robustness with respect to the ill-conditioned nature of the data matrix, can smoothly handle different levels of sparsity, and is modeled to include delays due to multi-path and the Doppler spread induced by the channel. We present numerical evidence over simulated as well as field data to compare the performance of our method to conventional sparse reconstruction techniques.

First-principles study of properties of Mn 2ZnMg alloy

We investigate the electronic structures and magnetic properties of Mn 2ZnMg compound with Hg 2CuTi-type structure using first-principles full-potential local orbital minimum basis calculations. Based on the analysis on the electronic structures, it is demonstrated that the compound is half-metallic antiferromagnet and the compound is favorable to form Hg 2CuTi-type structure instead of the conventional L2 1 one. The complicated hybridization among the p and d states dominates mainly the origin of the gap. The Fermi level ( E F ) shifts slightly with the lattice parameter changed. Spin–orbit coupling hardly reduces the degree of spin polarization of the density of states at the Fermi level.

Subsampled array design for compressive sensing.

Optimal array design is considered for beamforming using the compressive sensing technique. Compressive sensing is an l1 technique suitable for solving inverse problems that are highly undersampled and would be considered ill‐posed by conventional l2 methods. Consequently, fewer array elements are required to successfully detect targets and suppress sidelobes and noise. An array does not need to be spaced at half‐wavelength intervals, nor are the beams required to be orthonormal. In fact, compressive sampling works best when sample points are at apparently random intervals (satisfying the uniform uncertainty principle). This paper considers conventional, subsampled, random, logarithmic, and Golomb spaced arrays for the purpose of target detection via compressive sensing. [Work supported by the ONR.]

Way-lookup buffer for low-power set-associative cache

The way-predicting (WP) cache is simple and energy-efficient when used as L1 cache. However, a concern in the WP cache is the speed of the slow hit. This paper proposes the use of way-lookup buffer (WLB) cache to alleviate the problem of slow hit in the WP cache. The results show that the WLB slow hit is 33% faster than the WP slow hit in L1 cache. This is important because the slow hit is often the critical path that determines the processor clock cycle time. The WLB cache is as energy-efficient as the WP cache when used as L1 cache; using the WLB cache can reduce the power consumed by the conventional L1 cache by 68%. Therefore, if we like the nice properties (i.e., simplicity and energy efficiency) of the WP cache but are reluctant to use it because the slow hit extends the processor clock cycle time, then the WLB cache is the one to use.

Multiresolution Wavelet Decompositions for Content Based Image Retrieval.

We present a method for searching in an image database using a query image that is similar to the intended target. The query image may be a handdrawn sketch or a potentially low-quality) scan of the image to be retrieved. Our searching algorithm makes use of multiresolution wavelet decompositions of the query and database images. The coefficients of these decompositions are distilled into small “signatures” for each image. We introduce an “image querying metric” that operates on these signatures. This metric essentially compares how many significant wavelet coefficients the query has in common with potential targets. The metric includes parameters that can be tuned, using a statistical analysis, to accommodate the kinds of image distortions found in different types of image queries. The resulting algorithm is simple, requires very little storage overhead for the database of signatures, and is fast enough to be performed on a database of 20,000 images at interactive rates (on standard desktop machines) as a query is sketched. Our experiments with hundreds of queries in databases of 1000 and 20,000 images show dramatic improvement, in both speed and success rate, over using a conventional L1, L2, or color histogram norm. Patil Bhushan V. lecturer in E & T C Dept. R.C.Patel Institute Of Technology,Shirpur.(M.S) Shailaja A.Patil Lecturer in E & T C Dept. R.C.Patel Institute Of Technology,Shirpur (M.S) Smital D.Patil. lecturer in E & T C Dept. R.C.Patel Institute Of Technology,Shirpur.(M.S) Manisha B.Barawkar. lecutrer in E & T C Dept. R.C.Patel Institute Of Technolgy,Shirpur (M.S) The full text of the article is not available in the cache. Kindly refer the IJCA digital library at www.ijcaonline.org for the complete article. In case, you face problems while downloading the full-text, please send a mail to editor at editor@ijcaonline.org

Low-power L2 cache design for multi-core processors

A low-power set-associative L2 cache design for a multi-core processor is proposed. Since this way-predicting L2 cache (WP-L2) predicts a destination way and accesses only the predicted way, it consumes less energy than a conventional set-associative L2 cache. Exploiting access patterns of an L2 cache, WP-L2 is based on two prediction logics; a look-ahead buffer (LAB) predicts the next sequential cache block and a way-affinity table (WAT) records the way number of the previous L2 cache access. Combining the logics, WP-L2 predicts correct ways for about 83% of L2 cache accesses and reduces about 22% of access latency and 44% of energy consumption compared to the conventional eight-way set-associative L2 cache.

Performance Evaluation of Combined L1/L5 Kalman Filter-Based Tracking versus Standalone L1/L5 Tracking in Challenging Environments

The ever-increasing demand on GPS to perform in challenging environments is the main motivation behind this research. With the existence of these challenging environments, more research is directed towards enhancing the tracking capabilities. Several solutions have been proposed to enable high sensitivity tracking using only one signal. However, new GPS signals are now available, in addition to the conventional L1 signal. Being transmitted from the same space vehicle through the same environment, the errors between these signals are correlated. Hence, an increase in tracking sensitivity can be achieved by combining two or more of these signals. This paper proposes the idea of combining the L1 and L5 signals using one Kalman filter, where the correlator outputs of the two signals are used to estimate the tracking errors. The performance of this combined Kalman filter is compared to a similar Kalman filter that is used separately for tracking each of the two signals. The performance of both filters is compared in environments suffering urban canyon multipath, moderate ionospheric errors, in addition to a motion model of a typical vehicle. The combined Kalman filter is shown to outperform the separate Kalman filter, both in the tracking errors and in the filter statistics.

Leveraging Access Locality for the Efficient Use of Multibit Error-Correcting Codes in L2 Cache

It is almost evident that SRAM-based cache memories will be subject to a significant degree of parametric random defects if one wants to leverage the technology scaling to its full extent. Although strong multibit error-correcting codes (ECC) appear to be a natural choice to handle a large number of random defects, investigation of their applications in cache remains largely missing arguably because it is commonly believed that multibit ECC may incur prohibitive performance degradation and silicon/energy cost. By developing a cost-effective L2 cache architecture using multibit ECC, this paper attempts to show that, with appropriate cache architecture design, this common belief may not necessarily hold true for L2 cache. The basic idea is to supplement a conventional L2 cache core with several special-purpose small caches/buffers, which can greatly reduce the silicon cost and minimize the probability of explicitly executing multibit ECC decoding on the cache read critical path, and meanwhile, maintain soft error tolerance. Experiments show that, at the random defect density of 0.5 percent, this design approach can maintain almost the same instruction per cycle (IPC) performance over a wide spectrum of benchmarks compared with ideal defect-free L2 cache, while only incurring less than 3 percent of silicon area overhead and 36 percent power consumption overhead.

갈릴레오 GIOVE-A E1 신호 분석 및 RF 프론트엔드 대역폭 영향 분석

갈릴레오는 유럽에서 개발하고 있는 민간 주도의 새로운 위성항법시스템이다. 갈릴레오 시스템의 성능을 테스트하기 위한 테스트 위성인 GIOVE-A 위성이 현재 지구 상 궤도에서 항법신호를 송출하고 있다. 갈릴레오 시스템의 성능을 평가하고 향후 갈릴레오 수신기를 개발하기 위해서는 GIOVE-A 위성 신호를 분석할 필요가 있다. 본 논문에서는 GIOVE-A 위성신호를 수신 한 후 이를 GIOVE-A 신호 규격문서에 정의된 정보를 이용하여 신호 처리하였다. 신호 획득, 추적 및 항법 메시지 디코딩의 과정을 수행하여 현재 GIOVE-A 위성의 항법정보 제공 상황을 파악하였다. BOC 신호 사용에 따른 항법신호 대역폭의 증가가 갈릴레오 신호가 GPS와 다른 점 중의 하나이며 RF 프론트엔드의 대역폭에 따른 항법 신호 수신 성능을 평가하여 현재 사용되고 있는 GPS L1 RF 프론트엔드를 사용할 수 있는지를 검토하였다. Galileo is a new civil Global Navigation Satellite System(GNSS) developed by Europe. GIOVE-A, a satellite to test Galileo system performance, transmits navigation signal on orbit. Evaluation of Galileo system and development of Galileo receiver needs to analyze GIOVE-A signals. In this paper, we received GIOVE-A signals and processed it using GIOVE-A Interface Control Document(ICD). Signal acquisition, tracking and navigation message decoding made grasping current signal status possible. Bandwidth increase by BOC modulation is one of the difference from GPS. Therefore, we investigated feasibility of conventional GPS L1 RF front-end to receive GIOVE-A E1 signal by evaluation of receiving performance of navigation signal on each bandpass filter of RF front-end.

A distributed spatio-temporal EEG/MEG inverse solver

We propose a novel ℓ1ℓ2-norm inverse solver for estimating the sources of EEG/MEG signals. Based on the standard ℓ1-norm inverse solvers, this sparse distributed inverse solver integrates the ℓ1-norm spatial model with a temporal model of the source signals in order to avoid unstable activation patterns and “spiky” reconstructed signals often produced by the currently used sparse solvers. The joint spatio-temporal model leads to a cost function with an ℓ1ℓ2-norm regularizer whose minimization can be reduced to a convex second-order cone programming (SOCP) problem and efficiently solved using the interior-point method. The efficient computation of the SOCP problem allows us to implement permutation tests for estimating statistical significance of the inverse solution. Validation with simulated and human MEG data shows that the proposed solver yields source time course estimates qualitatively similar to those obtained through dipole fitting, but without the need to specify the number of dipole sources in advance. Furthermore, the ℓ1ℓ2-norm solver achieves fewer false positives and a better representation of the source locations than the conventional ℓ2 minimum-norm estimates.