OpenPARF: An Open-Source Placement and Routing Framework for Large-Scale Heterogeneous FPGAs with Deep Learning Toolkit

This paper proposes OpenPARF, an open-source placement and routing framework for large-scale FPGA designs. OpenPARF is implemented with the deep learning toolkit PyTorch and supports massive parallelization on GPU. The framework proposes a novel asymmetric multi-electrostatic field system to solve FPGA placement. It considers fine-grained routing resources inside configurable logic blocks (CLBs) for FPGA routing and supports large-scale irregular routing resource graphs. Experimental results on ISPD 2016 and ISPD 2017 FPGA contest benchmarks and industrial benchmarks demonstrate that OpenPARF can achieve 0.4-12.7% improvement in routed wirelength and more than $2\times$ speedup in placement. We believe that OpenPARF can pave the road for developing FPGA physical design engines and stimulate further research on related topics

Dwarf galaxies with the highest concentration are not thicker than ordinary dwarf galaxies

The formation mechanism of high-concentration dwarf galaxies is still a mystery. We perform a comparative study of the intrinsic shape of nearby low-mass galaxies with different stellar concentration. The intrinsic shape is parameterized by the intermediate-to-major axis ratios B/A and the minor-to-major axis ratios C/A of triaxial ellipsoidal models. Our galaxies ($10^{7.5} M_\odot$ < $M_\star$ < $10^{10.0} M_\odot$) are selected to have spectroscopic redshift from SDSS or GAMA, and have broadband optical images from the HSC-SSP Wide layer survey. The deep HSC-SSP images allow to measure the apparent axis ratios $q$ at galactic radii beyond the central star-forming area of our galaxies. We infer the intrinsic axis ratios based on the $q$ distributions. We find that 1) our galaxies have typical intrinsic shape similarly close to be oblate ($\mu_{B/A}$ $\sim$ 0.9--1), regardless of the concentration, stellar mass, star formation activity, and local environment (being central or satellite); 2) galaxies with the highest concentration tend to have intrinsic thickness similar to or (in virtually all cases) slightly thinner (i.e. smaller mean $\mu_{C/A}$ or equivalently lower triaxiality) than ordinary galaxies, regardless of other properties explored here. This appears to be in contrast with the expectation of the classic merger scenario for high-concentration galaxies. Given the lack of a complete understanding of dwarf-dwarf merger, we cannot draw a definite conclusion about the relevance of mergers in the formation of high-concentration dwarfs. Other mechanisms such as halo spin may also play important roles in the formation of high-concentration dwarf galaxies.Comment: 12 pages, 8 figures, 2 tables, accepted for publication in Ap

The Size-Mass Relation of Post-Starburst Galaxies in the Local Universe

We present a study of the size--mass relation for local post-starburst (PSB) galaxies at $z\lesssim0.33$ selected from the Sloan Digital Sky Survey Data Release 8. We find that PSB galaxies with stellar mass ($M_*$) at $10^9~M_{\odot}<M_*<10^{12}~M_{\odot}$ have their galaxy size smaller than or comparable with those of quiescent galaxies (QGs). After controlling redshift and stellar mass, the sizes of PSBs are $\sim 13\%$ smaller on average than those of QGs, such differences become larger and significant towards the low-$M_*$ end, especially at $10^{9.5}~M_{\odot} \lesssim M_*\lesssim 10^{10.5}~M_{\odot}$ where PSBs can be on average $\sim 19\%$ smaller than QGs. In comparison with predictions of possible PSB evolutionary pathways from cosmological simulations, we suggest that a fast quenching of star formation following a short-lived starburst event (might be induced by major merger) should be the dominated pathway of our PSB sample. Furthermore, by cross-matching with group catalogs, we confirm that local PSBs at $M_*\lesssim10^{10}~M_{\odot}$ are more clustered than more massive ones. PSBs resided in groups are found to be slightly larger in galaxy size and more disk-like compared to field PSBs, which is qualitatively consistent with and thus hints the environment-driven fast quenching pathway for group PSBs. Taken together, our results support multiple evolutionary pathways for local PSB galaxies: while massive PSBs are thought of as products of fast quenching following a major merger-induced starburst, environment-induced fast quenching should play a role in the evolution of less massive PSBs, especially at $M_*\lesssim 10^{10}~M_{\odot}$.Comment: 16 pages, 7 figures; accepted for publication in Ap

On the use of one-step perturbation to investigate the dependence of NOE-derived atom-atom distance bound violations of peptides upon a variation of force-field parameters

The method of one-step perturbation can be used to predict from a single molecular dynamics simulation the values of observable quantities as functions of variations in the parameters of the Hamiltonian or biomolecular force field used in the simulation. The method is used to predict violations of nuclear overhauser effect (NOE) distance bounds measured in nuclear magnetic resonance (NMR) experiments by atom-atom distances of the NOE atom pairs when varying force-field parameters. Predictions of NOE distance bound violations between different versions of the GROMOS force field for a hexa-β-peptide in solution show that the technique works for rather large force-field parameter changes as well as for very different NOE bound violation patterns. The effect of changing individual force-field parameters on the NOE distance bound violations of the β-peptide and an α-peptide was investigated too. One-step perturbation, which in this case is equivalent to reweighting configurations, constitutes an efficient technique to predict many values of different quantities from a single conformational ensemble for a particular system, which makes it a powerful force-field development technique that easily reduces the number of required separate simulations by an order of magnitude

An investigation into the risk of population bias in deep learning autocontouring

Background and Purpose: To date, data used in the development of Deep Learning-based automatic contouring (DLC) algorithms have been largely sourced from single geographic populations. This study aimed to evaluate the risk of population-based bias by determining whether the performance of an autocontouring system is impacted by geographic population.Materials and methods: 80 Head Neck CT deidentified scans were collected from four clinics in Europe (n = 2) and Asia (n = 2). A single observer manually delineated 16 organs-at-risk in each. Subsequently, the data was contoured using a DLC solution, and trained using single institution (European) data. Autocontours were compared to manual delineations using quantitative measures. A Kruskal-Wallis test was used to test for any difference between populations. Clinical acceptability of automatic and manual contours to observers from each participating institution was assessed using a blinded subjective evaluation.Results: Seven organs showed a significant difference in volume between groups. Four organs showed statistical differences in quantitative similarity measures. The qualitative test showed greater variation in acceptance of contouring between observers than between data from different origins, with greater acceptance by the South Korean observers.Conclusion: Much of the statistical difference in quantitative performance could be explained by the difference in organ volume impacting the contour similarity measures and the small sample size. However, the qualitative assessment suggests that observer perception bias has a greater impact on the apparent clinical acceptability than quantitatively observed differences. This investigation of potential geographic bias should extend to more patients, populations, and anatomical regions in the future.</p