Truly Pre-Routing Timing Prediction via Considering Power Delivery Network

As the complexity increases, power delivery network (PDN) optimization becomes a more important step in a modern design. In order to construct a robust PDN, most classic PDN optimization methods focus on adjusting the dimensions of power stripes. However, this approach becomes infeasible when voltage violation regions also have severe routing congestion. Hence, this paper proposes a delicate procedure to insert additional power stripes to reduce voltage violation while maintaining routability. In the beginning, IR-drop high related regions are identified to reveal those locations which are thirsty for more currents. Then, we solve a minimum-cost flow problem to find the topologies of power delivery paths (PDPs) from power sources to these regions and determine the widths of edges in each PDP so that enough currents can be provided to these regions. Moreover, vertical power stripes (VPSs for short) are inserted to the locations which have less routing congestion and severe voltage violations by the dynamic programming to reduce a probability to deteriorate routability. Finally, more wires will be inserted to IR-drop high related regions if there still exist voltage violations. Experimental results show that our method can use much less routing resource and induce less routing congestion to meet IR-drop constraint in industry designs.

Power delivery network (PDN) design is already a challenging problem for single die designs using advanced process technologies. For systems created using stacked dies with TSVs, several additional issues that affect power delivery and reliability have to be addressed. In stacked die configuration, the dies higher up in the stack-up experience additional drop and noise in their power supply as it propagates through the TSV networks of one or more dies placed lower in the stack-up. For the lower dies, the presence of the TSV farm and associated metals/vias affects the homogeneity of their own power delivery network. And if multiple dies share power and ground domains, then there could be an inter-die propagation of supply noise. So it becomes important that a PDN design and optimization flow for stacked die designs models and analyzes the presence of multiple dies and also the switching and the noise impact from the dies on each other. The analysis could be concurrent or model based, depending upon whether full databases for all the chips are available or their electrically equivalent models such as Chip Power Model (CPM) are available, respectively. For both of these approaches, a DC and time-domain analysis has to be done at the chip layout level to accurately predict the power/ground noise in the stacked die design. These analyses need to done starting early, in order to enable prototyping and design trade-off decisions. In this paper, a prototyping and verification solution for multi-die TSV based designs is outlined along with results from various design decisions undertaken.

Multi-modal fusion model for Time-Varying medical Data: Addressing Long-Term dependencies and memory challenges in sequence fusion.

PurposeThe first transmission of coronavirus to humans started in Wuhan city of China, took the shape of a pandemic called Corona Virus Disease 2019 (COVID-19), and posed a principal threat to the entire world. The researchers are trying to inculcate artificial intelligence (Machine learning or deep learning models) for the efficient detection of COVID-19. This research explores all the existing machine learning (ML) or deep learning (DL) models, used for COVID-19 detection which may help the researcher to explore in different directions. The main purpose of this review article is to present a compact overview of the application of artificial intelligence to the research experts, helping them to explore the future scopes of improvement.MethodsThe researchers have used various machine learning, deep learning, and a combination of machine and deep learning models for extracting significant features and classifying various health conditions in COVID-19 patients. For this purpose, the researchers have utilized different image modalities such as CT-Scan, X-Ray, etc. This study has collected over 200 research papers from various repositories like Google Scholar, PubMed, Web of Science, etc. These research papers were passed through various levels of scrutiny and finally, 50 research articles were selected.ResultsIn those listed articles, the ML / DL models showed an accuracy of 99% and above while performing the classification of COVID-19. This study has also presented various clinical applications of various research. This study specifies the importance of various machine and deep learning models in the field of medical diagnosis and research.ConclusionIn conclusion, it is evident that ML/DL models have made significant progress in recent years, but there are still limitations that need to be addressed. Overfitting is one such limitation that can lead to incorrect predictions and overburdening of the models. The research community must continue to work towards finding ways to overcome these limitations and make machine and deep learning models even more effective and efficient. Through this ongoing research and development, we can expect even greater advances in the future.

High Level Synthesis (HLS) has many productivity advantages over traditional RTL design, but routing congestion is difficult to resolve due to the lack of physical information in HLS. In this paper we propose a novel design flow by integrating a HLS tool with physically aware logic synthesis technology. Using this approach, one can discover congestion problems early and trace their sources to specific parts of the input SystemC models. This allows designers to resolve the congestion problems before going to the layout design phase. We applied this flow to a large-scale HLS production design with results showing that this flow can significantly improve not only routing congestion but design area and timing as well.

Machine learning is frequently being leveraged to tackle problems in the health sector including utilization for clinical decision-support. Its use has historically been focused on single modal data. Attempts to improve prediction and mimic the multimodal nature of clinical expert decision-making has been met in the biomedical field of machine learning by fusing disparate data. This review was conducted to summarize the current studies in this field and identify topics ripe for future research. We conducted this review in accordance with the PRISMA extension for Scoping Reviews to characterize multi-modal data fusion in health. Search strings were established and used in databases: PubMed, Google Scholar, and IEEEXplore from 2011 to 2021. A final set of 128 articles were included in the analysis. The most common health areas utilizing multi-modal methods were neurology and oncology. Early fusion was the most common data merging strategy. Notably, there was an improvement in predictive performance when using data fusion. Lacking from the papers were clear clinical deployment strategies, FDA-approval, and analysis of how using multimodal approaches from diverse sub-populations may improve biases and healthcare disparities. These findings provide a summary on multimodal data fusion as applied to health diagnosis/prognosis problems. Few papers compared the outputs of a multimodal approach with a unimodal prediction. However, those that did achieved an average increase of 6.4% in predictive accuracy. Multi-modal machine learning, while more robust in its estimations over unimodal methods, has drawbacks in its scalability and the time-consuming nature of information concatenation.

The efficient design flow presented in this chapter is a crucial aspect of this work. Full custom design approaches succeed in enabling ultra-low energy operation, but are too time intensive for large-scale digital designs. Commercial standard cell libraries and very-large-scale-integration (VLSI) design flows enable a fast design methodology for large-scale digital designs, but fail to provide ultra-low energy operation. This chapter unifies these two. Chapter 2 presented a logic library which has proven to be performant under ultra-low-voltage variation-sensitive conditions. This chapter takes that logic library to the next level in digital design: a standard cell design flow. The goal is to keep the speed and energy performance of the full custom work referenced in Chap. 2. Until now, it lacked the ease of design, the fast design cycle, the predictability of performance and the optimization methodology of modern standard cell design (tool) flows. This chapter solves just that: the industry-standard commercial tool flow is leveraged to provide logic synthesis, timing analysis, place-and-route and detailed power analysis of the logic library presented in Chap. 2. Register-transfer modelling (RTL) can be used to describe functionality. After the logic library is characterized into logic, timing and power models, the logic synthesis can map the RTL to the logic library. A silicon-ready layout with performance verification can be realized during place-and-route.

In the era of artificial intelligence, machine learning methods are successfully used in various fields. Machine learning has attracted extensive attention from investors in the financial market, especially in stock price prediction. However, one argument for the machine learning methods used in stock price prediction is that they are black-box models which are difficult to interpret. In this paper, we focus on the future stock price prediction with the historical stock price by machine learning and deep learning methods, such as support vector machine (SVM), random forest (RF), Bayesian classifier (BC), decision tree (DT), multilayer perceptron (MLP), convolutional neural network (CNN), bi-directional long-short term memory (BiLSTM), the embedded CNN, and the embedded BiLSTM. Firstly, we manually design several financial time series where the future price correlates with the historical stock prices in pre-designed modes, namely the curve-shape-feature (CSF) and the non-curve-shape-feature (NCSF) modes. In the CSF mode, the future prices can be extracted from the curve shapes of the historical stock prices. Conversely, in the NCSF mode, they can’t. Secondly, we apply various algorithms to those pre-designed and real financial time series. We find that the existing machine learning and deep learning algorithms fail in stock price prediction because in the real financial time series, less information of future prices is contained in the CSF mode, and perhaps more information is contained in the NCSF. Various machine learning and deep learning algorithms are good at handling the CSF in historical data, which are successfully applied in image recognition and natural language processing. However, they are inappropriate for stock price prediction on account of the NCSF. Therefore, accurate stock price prediction is the key to successful investment, and new machine learning algorithms handling the NCSF series are needed.

Transfer learning enables prediction of steel corrosion in concrete under natural environments

While existing machine learning (ML) frameworks focus on established platforms, like running CUDA on server-grade GPUs, there have been growing demands to enable emerging AI applications in a broader set of scenarios, such as running Large Language Models (LLMs) within browsers and mobile phones. However, deploying emerging models on new platforms (such as Metal and WebGPU) presents significant software engineering challenges due to rapid model evolution and limited tooling and practices for these platforms. Previous practice for ML model deployment often follows a bottom-up fashion, where engineers first implement individual required operators and then put them together. However, this traditional development approach fails to meet the productivity requirements when deploying emerging ML applications, with the testing and debugging part as a bottleneck. To this end, we introduce TapML, a top-down approach designed to streamline model deployment on diverse platforms. While the traditional bottom-up approach requires crafting manual tests, TapML automatically creates high-quality, realistic test data through operator-wise test carving. Furthermore, TapML uses a migration-based strategy to gradually offload model implementation from the mature source platform to the target platform, minimizing the debugging scope of compound errors. TapML has been used as the default development method in the MLC-LLM project to deploy emerging ML models. Within 2 years, TapML has accelerated the deployment of 105 emerging models in 27 model architectures across 5 emerging platforms. We show that TapML effectively boosts developer productivity while ensuring the quality of deployed models. Furthermore, we summarize comprehensive case studies from our real-world development, offering best practices for developing emerging ML systems.

Technology node scaling is driven by the need to increase system performance, but it also leads to a significant power integrity bottleneck, due to the associated back-end-of-line (BEOL) scaling. Power integrity degradation induced by on-chip Power Delivery Network (PDN) IR drop is a result of increased power density and number of metal layers in the BEOL and their resistivity. Meanwhile, signal routing limits the SoC performance improvements due to increased routing congestion and delays. To conquer these issues, we introduce a disruptive technology: wafer backside (BS) connection to realize chip BS PDN (BSPDN) and BS signal routing. We first provide some key wafer processes features that were developed at imec to enable this technology. Further, we show benefits of this technology by demonstrating a large improvement in chip power integrity and performance after applying this technology to BSPDN and BS routing with a sub-2nm technology node design rule. Challenges and outlook of the BS technology are also discussed before conclusion of this paper.

Machine learning-based multimodal fusion recognition of passenger ship seafarers’ workload: A case study of a real navigation experiment

Electromigration (EM) effects are a high concern in power delivery networks where the current flow is unidirectional. As the integrated circuits (IC) technology nodes become smaller, the EM effects become a critical reliability failure mechanism also on signal nets. Such effect is also known as AC electromigration. This work presents a design strategy using special signal non-default routing rules (SSNDRs) to re-route the wire segments of critical nets that present a high current density and are EM-unsafe. Some EM-aware design steps are added in the traditional design flow to analyze, fix and improve the Mean Time to Failure (MTTF) of the designs.

In the era of big data, a large number of multimodal data are constantly generated and accumulated. In the practical application analysis of multimodal big data, the modal incompleteness, real-time processing, modal imbalance and high-dimensional attributes pose severe challenges to the multimodal fusion method. In view of the above characteristics of multimodal data and the shortcomings of existing multimodal fusion algorithms, this chapter studies the multimodal data fusion algorithms from four aspects: incomplete modal analysis fusion, incremental modal clustering fusion, heterogeneous modal migration fusion and low dimensional modal sharing fusion. A series of multimodal data fusion models and algorithms are designed.KeywordsMultimodal dataData fusionUnsupervised learning

In recent years, there have been many multimodal works in the field of remote sensing, and most of them have achieved good results in the task of land-cover classification. However, multi-scale information is seldom considered in the multi-modal fusion process. Secondly, the multimodal fusion task rarely considers the application of attention mechanism, resulting in a weak representation of the fused feature. In order to better use the multimodal data and reduce the losses caused by the fusion of different modalities, we proposed a TRMSF (Transformer and Multi-scale fusion) network for land-cover classification based on HSI (hyperspectral images) and LiDAR (Light Detection and Ranging) images joint classification. The network enhances multimodal information fusion ability by the method of attention mechanism from Transformer and enhancement using multi-scale information to fuse features from different modal structures. The network consists of three parts: multi-scale attention enhancement module (MSAE), multimodality fusion module (MMF) and multi-output module (MOM). MSAE enhances the ability of feature representation from extracting different multi-scale features of HSI, which are used to fuse with LiDAR feature, respectively. MMF integrates the data of different modalities through attention mechanism, thereby reducing the loss caused by the data fusion of different modal structures. MOM optimizes the network by controlling different outputs and enhances the stability of the results. The experimental results show that the proposed network is effective in multimodality joint classification.