HL-Pow: A Learning-Based Power Modeling Framework for High-Level Synthesis

High-level synthesis (HLS) enables designers to customize hardware designs efficiently. However, it is still challenging to foresee the correlation between power consumption and HLS-based applications at an early design stage. To overcome this problem, we introduce HL-Pow, a power modeling framework for FPGA HLS based on state-of-the-art machine learning techniques. HL-Pow incorporates an automated feature construction flow to efficiently identify and extract features that exert a major influence on power consumption, simply based upon HLS results, and a modeling flow that can build an accurate and generic power model applicable to a variety of designs with HLS. By using HL-Pow, the power evaluation process for FPGA designs can be significantly expedited because the power inference of HL-Pow is established on HLS instead of the time-consuming register-transfer level (RTL) implementation flow. Experimental results demonstrate that HL-Pow can achieve accurate power modeling that is only 4.67% (24.02 mW) away from onboard power measurement. To further facilitate power-oriented optimizations, we describe a novel design space exploration (DSE) algorithm built on top of HL-Pow to trade off between latency and power consumption. This algorithm can reach a close approximation of the real Pareto frontier while only requiring running HLS flow for 20% of design points in the entire design space.Comment: published as a conference paper in ASP-DAC 202

Bronchoscopic ethanol injection combined with cryotherapy is an effective treatment for benign airway stenosis caused by endotracheal intubation or tracheotomyc

The benign tracheal stenosis is a challenge in interventional pulmonary disease. Bronchoscopic ethanol injection (BEI) is always used in airway stenosis caused by malignant tracheal tumor. The efficacy and safety of BEI in benign airway stenosis has not been studied before. To compare the safety and efficacy between bronchoscopic icryotherapy and BEI combined with bronchoscopic cryotherapy in the treatment of benign tracheal stenosis. A retrospective study included 61 patients with tracheal stenosis caused by endotracheal intubation and tracheotomy from July 2010 to June 2015 was made. 33 patients received repeated bronchoscopic cryotherapy alone were in Group A, 29 patients underwent repeated cryotherapy combined with BEI were in Group B. Dyspnea index, tracheal diameter were collected before and after treatment. Efficacy and complications were compared in two groups. The changes of tracheal diameter, dyspnea index were significant before and after treatment in both groups (P < 0.05). The long-term cure rate was higher in group B than that in group A (100% vs 84.8%). The average duration for dilated airway stable was much shorter in group B than group A (166±28 days vs 278±32 days, P < 0.05). The average cryotherapy session performed in group B was significantly less than that in group A (22.1±4.7 vs 34.9±6.5, P < 0.05). Meanwhile the complications in group A were seldom, the incidence of complications related to BEI were low in group B (mild chest pain 7.1%, bleeding 3.6% and cough 10.7%). BEI combined with bronchoscopic cryotherapy is an effective minimally invasive choice for releasing the airway obstructive symptoms

Reconstruction of compressed spectral imaging based on global structure and spectral correlation

In this paper, a convolution sparse coding method based on global structure characteristics and spectral correlation is proposed for the reconstruction of compressive spectral images. The proposed method uses the convolution kernel to operate the global image, which can better preserve image structure information in the spatial dimension. To take full exploration of the constraints between spectra, the coefficients corresponding to the convolution kernel are constrained by the norm to improve spectral accuracy. And, to solve the problem that convolutional sparse coding is insensitive to low frequency, the global total-variation (TV) constraint is added to estimate the low-frequency components. It not only ensures the effective estimation of the low-frequency but also transforms the convolutional sparse coding into a de-noising process, which makes the reconstructing process simpler. Simulations show that compared with the current mainstream optimization methods (DeSCI and Gap-TV), the proposed method improves the reconstruction quality by up to 7 dB in PSNR and 10% in SSIM, and has a great improvement in the details of the reconstructed image

3D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose Estimation

Regression-based methods for 3D human pose estimation directly predict the 3D pose parameters from a 2D image using deep networks. While achieving state-of-the-art performance on standard benchmarks, their performance degrades under occlusion. In contrast, optimization-based methods fit a parametric body model to 2D features in an iterative manner. The localized reconstruction loss can potentially make them robust to occlusion, but they suffer from the 2D-3D ambiguity. Motivated by the recent success of generative models in rigid object pose estimation, we propose 3D-aware Neural Body Fitting (3DNBF) - an approximate analysis-by-synthesis approach to 3D human pose estimation with SOTA performance and occlusion robustness. In particular, we propose a generative model of deep features based on a volumetric human representation with Gaussian ellipsoidal kernels emitting 3D pose-dependent feature vectors. The neural features are trained with contrastive learning to become 3D-aware and hence to overcome the 2D-3D ambiguity. Experiments show that 3DNBF outperforms other approaches on both occluded and standard benchmarks. Code is available at https://github.com/edz-o/3DNBFComment: ICCV 2023, project page: https://3dnbf.github.io

Analysis of CO2 Emission for the Cement Manufacturing with Alternative Raw Materials: A LCA-based Framework

AbstractThe cement industry is a significant CO2 emitter mainly due to the calcinations of raw materials and the combustions of fuels. Some measures have been considered to reduce the CO2 emissions in cement industry, of which alternative raw materials are the most efficient practicing way. In this study, a LCA-based CO2 accounting framework with alternative raw materials was constructed to analyze the CO2 emissions from concrete with different kinds of low carbon substitution, within which cement production process was divided into six stages associated with the environmental impacts. A better routine is expected to understand the environmental hazards of cement products and to optimize the design to reduce adverse environmental impacts

A Survey for Graphic Design Intelligence

Graphic design is an effective language for visual communication. Using complex composition of visual elements (e.g., shape, color, font) guided by design principles and aesthetics, design helps produce more visually-appealing content. The creation of a harmonious design requires carefully selecting and combining different visual elements, which can be challenging and time-consuming. To expedite the design process, emerging AI techniques have been proposed to automatize tedious tasks and facilitate human creativity. However, most current works only focus on specific tasks targeting at different scenarios without a high-level abstraction. This paper aims to provide a systematic overview of graphic design intelligence and summarize literature in the taxonomy of representation, understanding and generation. Specifically we consider related works for individual visual elements as well as the overall design composition. Furthermore, we highlight some of the potential directions for future explorations.Comment: 10 pages, 2 figure