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  1. In this work, we propose a novel approach for the real-time estimation of chip-level spatial power maps for commercial Google Coral M.2 TPU chips based on a machine-learning technique for the first time. The new method can enable the development of more robust runtime power and thermal control schemes to take advantage of spatial power information such as hot spots that are otherwise not available. Different from the existing commercial multi-core processors in which real-time performance-related utilization information is available, the TPU from Google does not have such information. To mitigate this problem, we propose to use features that are related to the workloads of running different deep neural networks (DNN) such as the hyperparameters of DNN and TPU resource information generated by the TPU compiler. The new approach involves the offline acquisition of accurate spatial and temporal temperature maps captured from an external infrared thermal imaging camera under nominal working conditions of a chip. To build the dynamic power density map model, we apply generative adversarial networks (GAN) based on the workload-related features. Our study shows that the estimated total powers match the manufacturer's total power measurements extremely well. Experimental results further show that the predictions of power maps are quite accurate, with the RMSE of only 4.98\rm mW/mm^2, or 2.6\% of the full-scale error. The speed of deploying the proposed approach on an Intel Core i7-10710U is as fast as 6.9ms, which is suitable for real-time estimation. 
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  2. Abstract

    We use the Very Energetic Radiation Imaging telescope Array System (VERITAS) imaging air Cherenkov telescope array to obtain the first measured angular diameter ofβUMa at visual wavelengths using stellar intensity interferometry (SII) and independently constrain the limb-darkened angular diameter. The age of the Ursa Major moving group has been assessed from the ages of its members, including nuclear member Merak (βUMa), an A1-type subgiant, by comparing effective temperature and luminosity constraints to model stellar evolution tracks. Previous interferometric limb-darkened angular-diameter measurements ofβUMa in the near-infrared (Center for High Angular Resolution Astronomy (CHARA) Array, 1.149 ± 0.014 mas) and mid-infrared (Keck Nuller, 1.08 ± 0.07 mas), together with the measured parallax and bolometric flux, have constrained the effective temperature. This paper presents current VERITAS-SII observation and analysis procedures to derive squared visibilities from correlation functions. We fit the resulting squared visibilities to find a limb-darkened angular diameter of 1.07 ± 0.04 (stat) ± 0.05 (sys) mas, using synthetic visibilities from a stellar atmosphere model that provides a good match to the spectrum ofβUMa in the optical wave band. The VERITAS-SII limb-darkened angular diameter yields an effective temperature of 9700 ± 200 ± 200 K, consistent with ultraviolet spectrophotometry, and an age of 390 ± 29 ± 32 Myr, using MESA Isochrones and Stellar Tracks. This age is consistent with 408 ± 6 Myr from the CHARA Array angular diameter.

     
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    Free, publicly-accessible full text available April 26, 2025
  3. 2.5D chiplet-based technology promises an efficient integration technique for advanced designs with more functionality and higher performance. Temperature and related thermal optimization, heat removal are of critical importance for temperature-aware physical synthesis for chiplets. This paper presents a novel graph convolutional networks (GCN) architecture to estimate the thermal map of the 2.5D chiplet-based systems with the thermal resistance networks built by the compact thermal model (CTM). First, we take the total power of all chiplets as an input feature, which is a global feature. This additional global information can overcome the limitation that the GCN can only extract local information via neighborhood aggregation. Second, inspired by convolutional neural networks (CNN), we add skip connection into the GCN to pass the global feature directly across the hidden layers with the concatenation operation. Third, to consider the edge embedding feature, we propose an edge-based attention mechanism based on the graph attention networks (GAT). Last, with the multiple aggregators and scalers of principle neighborhood aggregation (PNA) networks, we can further improve the modeling capacity of the novel GCN. The experimental results show that the proposed GCN model can achieve an average RMSE of 0.31 K and deliver a 2.6$\times$ speedup over the fast steady-state solver of open-source {\it HotSpot} based on SuperLU. More importantly, the GCN model demonstrates more useful generalization or transferable capability. Our results show that the trained GCN can be directly applied to predict thermal maps of six unseen datasets with acceptable mean RMSEs of less than 0.67 K without retraining via inductive learning. 
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  4. Abstract

    In 2017 February, the blazar OJ 287 underwent a period of intense multiwavelength activity. It reached a new historic peak in the soft X-ray (0.3–10 keV) band, as measured by the Swift X-ray Telescope. This event coincides with a very-high-energy (VHE)γ-ray outburst that led VERITAS to detect emission above 100 GeV, with a detection significance of 10σ(from 2016 December 9 to 2017 March 31). The time-averaged VHEγ-ray spectrum was consistent with a soft power law (Γ = −3.81 ± 0.26) and an integral flux corresponding to ∼2.4% that of the Crab Nebula above the same energy. Contemporaneous data from multiple instruments across the electromagnetic spectrum reveal a complex flaring behavior, primarily in the soft X-ray and VHE bands. To investigate the possible origin of such an event, our study focuses on three distinct activity states: before, during, and after the 2017 February peak. The spectral energy distributions during these periods suggest the presence of at least two nonthermal emission zones, with the more compact one responsible for the observed flare. Broadband modeling results and observations of a new radio knot in the jet of OJ 287 in 2017 are consistent with a flare originating from a strong recollimation shock outside the radio core.

     
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  5. Abstract

    G106.3+2.7, commonly considered to be a composite supernova remnant (SNR), is characterized by a boomerang-shaped pulsar wind nebula (PWN) and two distinct (“head” and “tail”) regions in the radio band. A discovery of very-high-energy gamma-ray emission (Eγ> 100 GeV) followed by the recent detection of ultrahigh-energy gamma-ray emission (Eγ> 100 TeV) from the tail region suggests that G106.3+2.7 is a PeVatron candidate. We present a comprehensive multiwavelength study of the Boomerang PWN (100″ around PSR J2229+6114) using archival radio and Chandra data obtained two decades ago, a new NuSTAR X-ray observation from 2020, and upper limits on gamma-ray fluxes obtained by Fermi-LAT and VERITAS observatories. The NuSTAR observation allowed us to detect a 51.67 ms spin period from the pulsar PSR J2229+6114 and the PWN emission characterized by a power-law model with Γ = 1.52 ± 0.06 up to 20 keV. Contrary to the previous radio study by Kothes et al., we prefer a much lower PWNB-field (B∼ 3μG) and larger distance (d∼ 8 kpc) based on (1) the nonvarying X-ray flux over the last two decades, (2) the energy-dependent X-ray size of the PWN resulting from synchrotron burn-off, and (3) the multiwavelength spectral energy distribution (SED) data. Our SED model suggests that the PWN is currently re-expanding after being compressed by the SNR reverse shock ∼1000 yr ago. In this case, the head region should be formed by GeV–TeV electrons injected earlier by the pulsar propagating into the low-density environment.

     
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  6. null (Ed.)
    Electromigration (EM) becomes a major concern for VLSI circuits as the technology advances in the nanometer regime. With Korhonen equations, EM assessment for VLSI circuits remains challenged due to the increasing integrated density. VLSI multisegment interconnect trees can be naturally viewed as graphs. Based on this observation, we propose a new graph convolution network (GCN) model, which is called {\it EMGraph} considering both node and edge embedding features, to estimate the transient EM stress of interconnect trees. Compared with recently proposed generative adversarial network (GAN) based stress image-generation method, EMGraph model can learn more transferable knowledge to predict stress distributions on new graphs without retraining via inductive learning. Trained on the large dataset, the model shows less than 1.5% averaged error compared to the ground truth results and is orders of magnitude faster than both COMSOL and state-of-the-art method. It also achieves smaller model size, 4X accuracy and 14X speedup over the GAN-based method. 
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  7. In this paper, we propose an image generative learning framework for electrostatic analysis for VLSI dielectric aging estimation. This work leverages the observation that the synthesized multi layer interconnect VLSI layout can be viewed as layered 2D images and the analysis can be viewed as the image generation. The efficient image-to-image translation property of generative learning is therefore used to obtain the potential distribution on the respective interconnect layers. Compared with the recent CNN-based electrostatic analysis method, the new method can lead to 1.54x speedup for inference due to reduced neural network structures and parameters. We demonstrate the proposed method for time-dependent dielectric breakdown analysis and show the significant speedup compared to the traditional numerical method. 
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