Logic Design and Power Optimization of Floating-Point Multipliers

Under IEEE-754 standard, for the current situation of excessive time and power consumption of multiplication operations in single-precision floating-point operations, the expanded boothwallace algorithm is used, and the partial product caused by booth coding is rounded and predicted with the symbolic expansion idea, and the partial product caused by single-precision floating-point multiplication and the accumulation of partial products are optimized, and the flowing water is used to improve the throughput. Based on this, a series of verification and synthesis simulations are performed using the SMIC-7 nm standard cell process. It is verified that the new single-precision floating-point multiplier can achieve a smaller power share compared to the conventional single-precision floating-point multiplier.

High accuracy wide field imaging method in radio interferometry

With the development of modern radio interferometers, wide-field continuum surveys have been planned and undertaken, for which accurate wide-field imaging methods are essential. Based on the widely-used W-stacking method, we propose a new wide-field imaging algorithm that can synthesize visibility data from a model of the sky brightness via degridding, able to construct dirty maps from measured visibility data via gridding. Results carry the smallest approximation error yet achieved relative to the exact calculation involving the direct Fourier transform. In contrast to the original W-stacking method, the new algorithm performs least-misfit optimal gridding (and degridding) in all three directions, and is capable of achieving much higher accuracy than is feasible with the original algorithm. In particular, accuracy at the level of single precision arithmetic is readily achieved by choosing a least-misfit convolution function of width W = 7 and an image cropping parameter of x0 = 0.25. If the accuracy required is only that attained by the original W-stacking method, the computational cost for both the gridding and FFT steps can be substantially reduced using the proposed method by making an appropriate choice of the width and image cropping parameters.

A Low-Cost High Radix Floating-Point Square-Root Circuit

In this paper, we propose an efficient architecture of floating-point square-root circuit with low area cost, which is in accordance with the IEEE-754 standard. We extend the principle of the standard SRT algorithm so that the latency and area cost of the proposed circuit are linear with the radix. In addition, no extra computation cycles are required. With 65 nm technology, the area cost of the single-precision floating-point square-root circuit based on proposed architecture is only 6450.84 μm2, and the dynamic power consumption is only 0.764 mW at 300 MHz. The implementation results show that the proposed square-root circuit can reduce the area cost by 60%~90% compared with other designs in the literature.

Annotating precision for integrative structural models using deep learning

Motivation: Integrative modeling of macromolecular structures usually results in an ensemble of models that satisfy the input information. The model precision, or variability among these models is estimated globally, i.e., a single precision value is reported for the model. However, it would be useful to identify regions of high and low precision. For instance, low-precision regions can suggest where the next experiments could be performed and high-precision regions can be used for further analysis, e.g., suggesting mutations. Results: We develop PrISM (Precision for Integrative Structural Models), using autoencoders to efficiently and accurately annotate precision for integrative models. The method is benchmarked and tested on five examples of binary protein complexes and five examples of large protein assemblies. The annotated precision is shown to be consistent with, and more informative than localization densities. The generated networks are also interpreted by gradient-based attention analysis. Availability: Source code is at https://github.com/isblab/prism.