Monad: Towards Cost-effective Specialization for Chiplet-based Spatial Accelerators

Advanced packaging offers a new design paradigm in the post-Moore era, where many small chiplets can be assembled into a large system. Based on heterogeneous integration, a chiplet-based accelerator can be highly specialized for a specific workload, demonstrating extreme efficiency and cost reduction. To fully leverage this potential, it is critical to explore both the architectural design space for individual chiplets and different integration options to assemble these chiplets, which have yet to be fully exploited by existing proposals. This paper proposes Monad, a cost-aware specialization approach for chiplet-based spatial accelerators that explores the tradeoffs between PPA and fabrication costs. To evaluate a specialized system, we introduce a modeling framework considering the non-uniformity in dataflow, pipelining, and communications when executing multiple tensor workloads on different chiplets. We propose to combine the architecture and integration design space by uniformly encoding the design aspects for both spaces and exploring them with a systematic ML-based approach. The experiments demonstrate that Monad can achieve an average of 16% and 30% EDP reduction compared with the state-of-the-art chiplet-based accelerators, Simba and NN-Baton, respectively.Comment: To be published in ICCAD 202

WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation

This paper explores the properties of the plain Vision Transformer (ViT) for Weakly-supervised Semantic Segmentation (WSSS). The class activation map (CAM) is of critical importance for understanding a classification network and launching WSSS. We observe that different attention heads of ViT focus on different image areas. Thus a novel weight-based method is proposed to end-to-end estimate the importance of attention heads, while the self-attention maps are adaptively fused for high-quality CAM results that tend to have more complete objects. Besides, we propose a ViT-based gradient clipping decoder for online retraining with the CAM results to complete the WSSS task. We name this plain Transformer-based Weakly-supervised learning framework WeakTr. It achieves the state-of-the-art WSSS performance on standard benchmarks, i.e., 78.4% mIoU on the val set of PASCAL VOC 2012 and 50.3% mIoU on the val set of COCO 2014. Code is available at https://github.com/hustvl/WeakTr.Comment: 20 pages, 11 figure

Decomposition of carbon emission driving factors and judgment of peak status in countries along the Belt and Road

Most of the countries along the Belt and Road are still developing, with their carbon emissions yet to peak. There is a lack of comprehensive analysis and research to judge these countries' current carbon peak state and quantify key driving factors contributing to their carbon emissions. This study aims to fill this gap.A new method for judging a country's peak carbon status based on a time series of carbon emissions is developed. We divide the status of all countries along the Belt and Road into four categories: reached the peak, peak plateau period 1 (the downward trend is not significant), peak plateau period 2 (obvious recession), and not reached the peak. LMDI factorization is used to decompose the change in carbon emissions of energy consumption into multiple factors: carbon intensity, energy intensity, economic output, and population size, based on Kaya's identity theory. The carbon emission and socioeconomic databases from 2000 to 2019 are utilized for this analysis. The main positive driving factor of the three countries (Hungary, Romania, Czech Republic) that have reached the peak is GDP PPP per population, while other driving factors make negative contributions to carbon emissions. In some years, these countries briefly experienced a negative contribution of GDP PPP per population to carbon emissions. The driving factors of carbon emissions for countries in the peak plateau period are not stable, with contributions of GDP PPP per population, energy intensity, and carbon intensity fluctuating periodically. In countries that have not reached the peak of carbon emissions, population growth and economic growth are significant positive contributors, while the effect of driving factors that negatively contribute to carbon emissions is less obvious.The study's findings provide valuable insights into the carbon emission peak status and driving factors of countries along the Belt and Road, which can be used to guide policymaking and future research in addressing climate change and promoting sustainable development in these regions

Exploiting Counter-Examples for Active Learning with Partial labels

This paper studies a new problem, \emph{active learning with partial labels} (ALPL). In this setting, an oracle annotates the query samples with partial labels, relaxing the oracle from the demanding accurate labeling process. To address ALPL, we first build an intuitive baseline that can be seamlessly incorporated into existing AL frameworks. Though effective, this baseline is still susceptible to the \emph{overfitting}, and falls short of the representative partial-label-based samples during the query process. Drawing inspiration from human inference in cognitive science, where accurate inferences can be explicitly derived from \emph{counter-examples} (CEs), our objective is to leverage this human-like learning pattern to tackle the \emph{overfitting} while enhancing the process of selecting representative samples in ALPL. Specifically, we construct CEs by reversing the partial labels for each instance, and then we propose a simple but effective WorseNet to directly learn from this complementary pattern. By leveraging the distribution gap between WorseNet and the predictor, this adversarial evaluation manner could enhance both the performance of the predictor itself and the sample selection process, allowing the predictor to capture more accurate patterns in the data. Experimental results on five real-world datasets and four benchmark datasets show that our proposed method achieves comprehensive improvements over ten representative AL frameworks, highlighting the superiority of WorseNet. The source code will be available at \url{https://github.com/Ferenas/APLL}.Comment: 29 pages, Under revie

Nonisolated switching-capacitor-integrated three- port converters with seamless PWM/PFM modulation

Efficiency and power density of power converters for interfacing photovoltaic panels, energy storage components such as batteries, and loads in photovoltaic (PV) systems become more and more important. Compared with individual converter design for different terminals, power-integrated multiport converters shows obvious advantages in simplifying the system structure, reducing the component count, and improving the operation reliability. Originated from the high power-density switched capacitor topology, a nonisolated switching-capacitor-integrated three-port converter (SCI-TPC) is presented to achieve single-stage direct power conversion among three ports. In order to minimize the cross-regulation effect, pulse-width-modulation (PWM) and pulse-frequency-modulation (PFM) are adopted to realize the flexible power regulation and achieve power balance among three ports. Main operation modes, power flow distribution, and power transfer characteristic are analyzed. With the seamless PWM and PFM hybrid modulation, the current stress can be reduced and the overall conversion efficiency over a full operating range can be improved. Main experimental results are provided to validate the effectiveness of the proposed concept

Ginsenoside Rb1 Prevents H2O2-Induced HUVEC Senescence by Stimulating Sirtuin-1 Pathway

We have previously reported that Ginsenoside Rb1 may effectively prevent HUVECs from senescence, however, the detailed mechanism has not demonstrated up to now. Recent studies have shown that sirtuin-1 (Sirt1) plays an important role in the development of endothelial senescence. The purpose of this study was to explore whether Sirt1 is involved in the action of Ginsenoside Rb1 regarding protection against H2O2-induced HUVEC Senescence.Senescence induced by hydrogen peroxide (H2O2) in human umbilical vein endothelial cells (HUVECs) was examined by analyzing plasminogen activator inhibitor-1 (PAI-1) expression, cell morphology, and senescence-associated beta-galactosidase (SA-β-gal) activity. The results revealed that 42% of control-treated HUVECs were SA-β-gal positive after treatment by 60 µmol/L H2O2, however, this particular effect of H2O2 was decreased more than 2-fold (19%) in the HUVECs when pretreated with Rb1 (20 µmol/L) for 30 min. Additionally, Rb1 decreased eNOS acetylation, as well as promoted more NO production that was accompanied by an increase in Sirt1 expression. Furthermore, upon knocking down Sirt1, the effect of Rb1 on HUVEC senescence was blunted.The present study indicated that Ginsenoside Rb1 acts through stimulating Sirt1 in order to protect against endothelial senescence and dysfunction. As such, Sirt1 appears to be of particular importance in maintaining endothelial functions and delaying vascular aging