More Like this

1. Full-Chip Power Density and Thermal Map Characterization for Commercial Microprocessors under Heat Sink Cooling

   https://doi.org/10.1109/TCAD.2021.3088081  

   Zhang, Jinwei; Sadiqbatcha, Sheriff; OrDea, Michael; Amrouch, Hussam; Tan, Sheldon X.-D. (January 2021, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems)

   null (Ed.)

   In this article, we address the problem of accurate full-chip power and thermal map estimation for commercial off-the-shelf multi-core processors. Processors operating with heat sink cooling remains a challenging problem due to the difficulty in direct measurement. We first propose an accurate full-chip steady-state power density map estimation method for commercial multi-core microprocessors. The new method consists of a few steps. First, 2D spatial Laplace operation is performed on the measured thermal maps (images) without heat sink to obtain the so-called "raw power maps". Then, a novel scheme is developed to generate the true power density maps from the raw power density maps. The new approach is based on thermal measurements of the processor with back-side cooling using an advanced infrared (IR) thermal imaging system. FEM thermal model constructed in COMSOL Multiphysics is used to validate the estimated power density maps and thermal conductivity. Later, this work creates a high-fidelity FEM thermal model with heat sink and reconstructs the full-chip thermal maps while the heat sink is on. Ensuring that power maps are similar under back cooling and heat sink cooling settings, the reconstructed thermal maps are verified by the matching between the on-chip thermal sensor readings and the corresponding elements of thermal maps. Experiments on an Intel i7-8650U 4-core processor with back cooling shows 96\% similarity (2D correlation) between the measured thermal maps and the thermal maps reconstructed from the estimated power maps, with 1.3$$\rm ^\circ$$C average absolute error. Under heat sink cooling, the average absolute error is 2.2$$\rm ^\circ$$C over a 56$$\rm ^\circ$$C temperature range and about 3.9\% error between the computed and the real thermal maps at the sensor locations. Furthermore, the proposed power map estimation method achieves higher resolution and at least 100$$\times$$ speedup than a recently proposed state-of-art Blind Power Identification method. 

   more » « less
2. Evaluation of the performance of classification algorithms for XFEL single-particle imaging data

   https://doi.org/10.1107/s2052252519001854  

   Shi, Yingchen; Yin, Ke; Tai, Xuecheng; DeMirci, Hasan; Hosseinizadeh, Ahmad; Hogue, Brenda G; Li, Haoyuan; Ourmazd, Abbas; Schwander, Peter; Vartanyants, Ivan A; et al (March 2019, IUCrJ)

   Using X-ray free-electron lasers (XFELs), it is possible to determine three-dimensional structures of nanoscale particles using single-particle imaging methods. Classification algorithms are needed to sort out the single-particle diffraction patterns from the large amount of XFEL experimental data. However, different methods often yield inconsistent results. This study compared the performance of three classification algorithms: convolutional neural network, graph cut and diffusion map manifold embedding methods. The identified single-particle diffraction data of the PR772 virus particles were assembled in the three-dimensional Fourier space for real-space model reconstruction. The comparison showed that these three classification methods lead to different datasets and subsequently result in different electron density maps of the reconstructed models. Interestingly, the common dataset selected by these three methods improved the quality of the merged diffraction volume, as well as the resolutions of the reconstructed maps. 

   more » « less
3. Dark Energy Survey Year 3 results: Curved-sky weak lensing mass map reconstruction

   https://doi.org/10.1093/mnras/stab1495  

   Jeffrey, N; Gatti, M; Chang, C; Whiteway, L; Demirbozan, U; Kovacs, A; Pollina, G; Bacon, D; Hamaus, N; Kacprzak, T; et al (June 2021, Monthly Notices of the Royal Astronomical Society)

   null (Ed.)

   ABSTRACT We present reconstructed convergence maps, mass maps, from the Dark Energy Survey (DES) third year (Y3) weak gravitational lensing data set. The mass maps are weighted projections of the density field (primarily dark matter) in the foreground of the observed galaxies. We use four reconstruction methods, each is a maximum a posteriori estimate with a different model for the prior probability of the map: Kaiser–Squires, null B-mode prior, Gaussian prior, and a sparsity prior. All methods are implemented on the celestial sphere to accommodate the large sky coverage of the DES Y3 data. We compare the methods using realistic ΛCDM simulations with mock data that are closely matched to the DES Y3 data. We quantify the performance of the methods at the map level and then apply the reconstruction methods to the DES Y3 data, performing tests for systematic error effects. The maps are compared with optical foreground cosmic-web structures and are used to evaluate the lensing signal from cosmic-void profiles. The recovered dark matter map covers the largest sky fraction of any galaxy weak lensing map to date. 

   more » « less
4. Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch

   https://doi.org/10.14778/3717755.3717776  

   Bajaj, Saurabh; Son, Hojae; Liu, Juelin; Guan, Hui; Serafini, Marco (December 2024, Proceedings of the VLDB Endowment)

   Graph Neural Networks (GNNs) have gained significant attention in recent years due to their ability to learn representations of graph-structured data. Two common methods for training GNNs are mini-batch training and full-graph training. Since these two methods require different training pipelines and systems optimizations, two separate classes of GNN training systems emerged, each tailored for one method. Works that introduce systems belonging to a particular category predominantly compare them with other systems within the same category, offering limited or no comparison with systems from the other category. Some prior work also justifies its focus on one specific training method by arguing that it achieves higher accuracy than the alternative. The literature, however, has incomplete and contradictory evidence in this regard. In this paper, we provide a comprehensive empirical comparison of representative full-graph and mini-batch GNN training systems. We find that the mini-batch training systems consistently converge faster than the full-graph training ones across multiple datasets, GNN models, and system configurations. We also find that minibatch training techniques converge to similar to or often higher accuracy values than full-graph training ones, showing that minibatch sampling is not necessarily detrimental to accuracy. Our work highlights the importance of comparing systems across different classes, using time-to-accuracy rather than epoch time for performance comparison, and selecting appropriate hyperparameters for each training method separately. 

   more » « less
5. Efficient and Accurate Mapping of Subsurface Anatomy via Online Trajectory Optimization for Robot Assisted Surgery

   https://doi.org/10.1109/ICRA57147.2024.10610456  

   Cho, Brian Y; Kuntz, Alan (May 2024, IEEE International Conference on Robotics and Automation (ICRA))

   Robotic surgical subtask automation has the potential to reduce the per-patient workload of human surgeons. There are a variety of surgical subtasks that require geometric information of subsurface anatomy, such as the location of tumors, which necessitates accurate and efficient surgical sensing. In this work, we propose an automated sensing method that maps 3D subsurface anatomy to provide such geometric knowledge. We model the anatomy via a Bayesian Hilbert map-based probabilistic 3D occupancy map. Using the 3D occupancy map, we plan sensing paths on the surface of the anatomy via a graph search algorithm, A * search, with a cost function that enables the trajectories generated to balance between exploration of unsensed regions and refining the existing probabilistic understanding. We demonstrate the performance of our proposed method by comparing it against 3 different methods in several anatomical environments including a real-life CT scan dataset. The experimental results show that our method efficiently detects relevant subsurface anatomy with shorter trajectories than the comparison methods, and the resulting occupancy map achieves high accuracy. 

   more » « less