Fast Logic Function Extraction of LUT from Bitstream in Xilinx FPGA

This paper presents a fast method to extract logic functions of look-up tables (LUTs) from a bitstream in Xilinx FPGAs. In general, FPGAs utilize LUTs as a primary resource to realize a logic function, and a typical N-input LUT comprises 2N 1-bit SRAM and N – 1 multiplexers. Whereas the previous research demands 2N exhaustive processing to find a mapping rule between an LUT and a bitstream, the proposed method decreases the processing to 2N by eliminating unnecessary processing. Experimental results show that the proposed method can reduce reversing time by more than 57% and 85% for Xilinx Spartan-3 and Virtex-5 compared to the previous exhaustive algorithm. It is noticeable that the reduction time becomes more significant as a commercial Xilinx FPGA tends to include a more tremendous number of LUTs.

Predicting Search Performance in Heterogeneous Visual Search Scenes with Real-World Objects

Previous work in our lab has demonstrated that efficient visual search with a fixed target has a reaction time by set size function that is best characterized by logarithmic curves. Further, the steepness of these logarithmic curves is determined by the similarity between target and distractor items (Buetti et al., 2016). A theoretical account of these findings was proposed, namely that a parallel, unlimited capacity, exhaustive processing architecture is underlying such data. Here, we conducted two experiments to expand these findings to a set of real-world stimuli, in both homogeneous and heterogeneous search displays. We used computational simulations of this architecture to identify a way to predict RT performance in heterogeneous search using parameters estimated from homogeneous search data. Further, by examining the systematic deviation from our predictions in the observed data, we found evidence that early visual processing for individual items is not independent. Instead, items in homogeneous displays seemed to facilitate each other’s processing by a multiplicative factor. These results challenge previous accounts of heterogeneity effects in visual search, and demonstrate the explanatory and predictive power of an approach that combines computational simulations and behavioral data to better understand performance in visual search.

Feature- and Object-based Attentional Modulation in the Human Auditory “Where” Pathway

Attending to a visual stimulus feature, such as color or motion, enhances the processing of that feature in the visual cortex. Moreover, the processing of the attended object's other, unattended, features is also enhanced. Here, we used functional magnetic resonance imaging to show that attentional modulation in the auditory system may also exhibit such feature- and object-specific effects. Specifically, we found that attending to auditory motion increases activity in nonprimary motion-sensitive areas of the auditory cortical “where” pathway. Moreover, activity in these motion-sensitive areas was also increased when attention was directed to a moving rather than a stationary sound object, even when motion was not the attended feature. An analysis of effective connectivity revealed that the motion-specific attentional modulation was brought about by an increase in connectivity between the primary auditory cortex and nonprimary motion-sensitive areas, which, in turn, may have been mediated by the paracingulate cortex in the frontal lobe. The current results indicate that auditory attention can select both objects and features. The finding of feature-based attentional modulation implies that attending to one feature of a sound object does not necessarily entail an exhaustive processing of the object's unattended features.