Jinwook Jung

NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy.

Near infrared spectroscopy (NIRS) is a non-invasive method to measure brain activity via changes in the degree of hemoglobin oxygenation through the intact skull. As optically measured hemoglobin signals strongly correlate with BOLD signals, simultaneous measurement using NIRS and fMRI promises a significant mutual enhancement of temporal and spatial resolutions. Although there exists a powerful statistical parametric mapping tool in fMRI, current public domain statistical tools for NIRS have several limitations related to the quantitative analysis of simultaneous recording studies with fMRI. In this paper, a new public domain statistical toolbox known as NIRS-SPM is described. It enables the quantitative analysis of NIRS signal. More specifically, NIRS data are statistically analyzed based on the general linear model (GLM) and Suns tube formula. The p-values are calculated as the excursion probability of an inhomogeneous random field on a representation manifold that is dependent on the structure of the error covariance matrix and the interpolating kernels. NIRS-SPM not only enables the calculation of activation maps of oxy-, deoxy-hemoglobin and total hemoglobin, but also allows for the super-resolution localization, which is not possible using conventional analysis tools. Extensive experimental results using finger tapping and memory tasks confirm the viability of the proposed method.

Wavelet minimum description length detrending for near-infrared spectroscopy

Near-infrared spectroscopy (NIRS) can be employed to investigate brain activities associated with regional changes of the oxy- and deoxyhemoglobin concentration by measuring the absorption of near-infrared light through the intact skull. NIRS is regarded as a promising neuroimaging modality thanks to its excellent temporal resolution and flexibility for routine monitoring. Recently, the general linear model (GLM), which is a standard method for functional MRI (fMRI) analysis, has been employed for quantitative analysis of NIRS data. However, the GLM often fails in NIRS when there exists an unknown global trend due to breathing, cardiac, vasomotion, or other experimental errors. We propose a wavelet minimum description length (Wavelet-MDL) detrending algorithm to overcome this problem. Specifically, the wavelet transform is applied to decompose NIRS measurements into global trends, hemodynamic signals, and uncorrelated noise components at distinct scales. The minimum description length (MDL) principle plays an important role in preventing over- or underfitting and facilitates optimal model order selection for the global trend estimate. Experimental results demonstrate that the new detrending algorithm outperforms the conventional approaches.

NIRS-SPM: statistical parametric mapping for near infrared spectroscopy

Even though there exists a powerful statistical parametric mapping (SPM) tool for fMRI, similar public domain tools are not available for near infrared spectroscopy (NIRS). In this paper, we describe a new public domain statistical toolbox called NIRS-SPM for quantitative analysis of NIRS signals. Specifically, NIRS-SPM statistically analyzes the NIRS data using GLM and makes inference as the excursion probability which comes from the random field that are interpolated from the sparse measurement. In order to obtain correct inference, NIRS-SPM offers the pre-coloring and pre-whitening method for temporal correlation estimation. For simultaneous recording NIRS signal with fMRI, the spatial mapping between fMRI image and real coordinate in 3-D digitizer is estimated using Horns algorithm. These powerful tools allows us the super-resolution localization of the brain activation which is not possible using the conventional NIRS analysis tools.

Energy-efficient Spin-Transfer Torque RAM cache exploiting additional all-zero-data flags

Large on-chip caches account for a considerable fraction of the total energy consumption in modern microprocessors. In this context, emerging Spin-Transfer Torque RAM (STT-RAM) has been regarded as a promising candidate to replace large on-chip SRAM caches in virtue of its nature of the zero leakage. However, large energy requirement of STT-RAM on write operations, resulting in a huge amount of dynamic energy consumption, precludes it from application to on-chip cache designs. In order to reduce the write energy of the STT-RAM cache thereby the total energy consumption, this paper provides an architectural technique which exploits the fact that many applications process a large number of zero data. The proposed design appends additional flags in cache tag arrays and set these additional bits if the corresponding data in the cache line is the zero-valued data in which all data bits are zero. Our experimental results show that the proposed cache design can reduce 73.78% and 69.30% of the dynamic energy on write operations at the byte and word granularities, respectively; total energy consumption reduced by 36.18% and 42.51%, respectively. In addition to the energy reduction, performance evaluation results indicate that the proposed cache improves the processor performance by 5.44% on average.

256-KB associativity-reconfigurable cache with 7T/14T SRAM for aggressive DVS down to 0.57 V

This paper presents a dependable cache memory for which associativity can be reconfigured dynamically. The proposed associativity-reconfigurable cache consists of pairs of cache ways. Each pair has two modes: the normal mode and the dependable mode. The proposed cache can dynamically enhance its reliability in the dependable mode, thereby trading off its performance. The reliability of the proposed cache can be scaled by reconfiguring its associativity. Moreover, the configuration can be chosen based upon current operating conditions. Our chip measurement results show that the proposed dependable cache possesses the scalable characteristic of reliability. Moreover, it can decrease the minimum operating voltage by 115 mV. The cycle accurate simulation shows that designing the L1, L2 caches using the proposed scheme results in 4.93% IPC loss on average. Area estimation results show that the proposed cache adds area overhead of 1.91% and 5.57% in 32-KB and 256-KB caches, respectively.

OpenDesign flow database: the infrastructure for VLSI design and design automation research

Recently, there have been a slew of design automation contests and released benchmarks. ISPD place & route contests, DAC placement contests, timing analysis contests at TAU and CAD contests at ICCAD are good examples in the past and more of new contests are planned in the upcoming conferences. These are interesting and important events that stimulate the research of the target problems and advance the cutting edge technologies. Nevertheless, most contests focus only on the point tool problems instead of addressing the design flow or co-optimization among design tools. OpenDesign Flow Database platform is developed to direct attentions to the overall design flow from logic synthesis to physical design optimization [1]. The goals are to provide an academic reference design flow based on past CAD contest results, the database for design benchmarks and point tool libraries, and standard design input/output formats to build a customized design flow by composing point tool libraries.

Wavelet-MDL based detrending method for near infrared spectroscopy (NIRS)

Near infrared spectroscopy (NIRS) is a relatively new non-invasive brain imaging method to measure brain activities associated with regional changes of the oxy- and deoxy- hemoglobin concentration. Typically, functional MRI or PET data are analyzed using the general linear model (GLM), in which measurements are modeled as a linear combination of explanatory variables plus an error term. However, the GLM often fails in NIRS if there exists an unknown global trend due to breathing, cardiac, vaso- motion and other experimental errors. In order to overcome these problems, we propose a wavelet-MDL based detrending algorithm. Specifically, the wavelet transform is applied to NIRS measurements to decompose them into global trends, signals and uncorrelated noise components in distinct scales. In order to prevent the over-fitting the minimum length description (MDL) principle is applied. Experimental results demonstrate that the new detrending algorithm outperforms the conventional approaches.

Physical synthesis of DNA circuits with spatially localized gates

With the current DNA nanotechnology, we are now able to arrange DNA molecules on a DNA origami to compose a logic gate. This in turn realizes a spatially localized DNA circuit, on which the logic gates are placed on the specific locations as in electronic circuits. In this paper, we address three key problems in designing large-scale spatially localized DNA circuits. An AND gate, made of four hairpins, functions in stochastic manner and sometimes outputs a wrong result. Given tolerable error probability at each circuit output, we address how the probability that each AND gate functions correctly can be determined, which in turn determines the location of constituent hairpins. In the second problem, we study how hairpins are arranged on a DNA origami to minimize the area of a whole circuit, which determines the area of the origami board. The third problem regards the DNA domain assignment so that connected gates can communicate without interference.

Pin Accessibility-Driven Cell Layout Redesign and Placement Optimization

The layout of standard cells is very dense these days, so some pins are hard to get access to. This is in particular true in complex cells with many pins (e.g. AOI) and in the layout where many of those cells are densely packed without much whitespace. We redesign those complex cells, so a library now contains both original cell and its new version with easier pin access; a systematic method is proposed to pick candidate cells for redesign and to dictate how redesign should be performed. We also introduce a measure of inaccessibility of pins in a cell, named IOC. Placement optimization is performed, which uses IOC to determine which cells should be replaced by its redesigned version and how whitespace should be redistributed. Experiments with 12 test circuits indicate that the number of routing errors (after the initial placement) is reduced by 82% on average, and the subsequent detailed routing takes 72% less runtime.

Redundant Via Insertion with Cut Optimization for Self-Aligned Double Patterning

Line-end cuts are employed to enable 1D gridded designs in self-aligned double patterning (SADP) process. Due to the minimum spacing constraints between adjacent cuts, cut optimization is important component. However, it brings a new challenge to redundant via (RV) insertion. As the cuts for RVs are not taken into account during line-end cut optimization, inserting some RVs may cause coloring conflicts or design rule violations. In this paper, we address a combined RV insertion and cut optimization problem. Given a via layout, the proposed approach optimizes the cuts in upper and lower metal layers, which are connected through a via, while RV candidate positions are considered. Only the RV candidates that do not incur coloring conflicts and design rule violations are chosen. Our experiments indicate that only 55.3% of vias receive RVs when cut optimization and RV insertion are performed separately; corresponding number increases to 86.4% when our approach is applied.