Miracle-dz/UniFAR-FTUs huggingface dataset:
Glass Interposer Integration of Logic and Memory Chiplets: PPA and Power/Signal Integrity Benefits

Glass interposers enable 3D stacking between the chiplets embedded into the substrate and the ones stacked directly on top, which is not possible in silicon. In this work, we demonstrate the benefits of such stacking in glass interposers over silicon in terms of key system-level metrics including area, wirelength, signal, power, and thermal integrity. We achieve this goal with GDS layouts of both chiplets and interposers and sign-off simulations. Our experiments show that glass offers 2.6X area, 21X wirelength, 17.72% full-chip power, 64.7% signal integrity, and 10X power integrity improvement over silicon at the cost of 15% increase in temperature.
Understanding Drivers’ Emotion Variations During NDRTs in Conditional Automated Driving Using EEG Data

Drivers’ emotion is one of the key factors affecting driving behavior, negative emotion would be harmful to safe driving. In conditional automated driving, drivers would devote the majority of the time to Non-Driving Related Tasks (NDRTs), and varying NDRTs may lead to variations in drivers’ emotions. In this study, a series of conditional automated driving simulation experiments considering four typical NDRTs was conducted, and the electroencephalogram (EEG) signals were detected to identify and characterize drivers’ emotions by the valence-arousal (V-A) model, where in valence ranges from displeasure to pleasure and arousal ranges from relaxation to excitement. The results show that 1) the drivers’ emotion, as measured by the valence and arousal, was significantly affected by the rest and reading tasks during conditional automated driving, but not influenced by the monitoring vehicle operation and watching video tasks; 2) the V-A model revealed that the drivers were prone to keep alert in automated driving while showing increased stress in the rest state. The findings of this study contribute to a better understanding of drivers’ emotions and NDRTs in conditional automated driving.
Fine-Grained QoS Control via Tightly-Coupled Bandwidth Monitoring and Regulation for FPGA-based Heterogeneous SoCs

Embedded systems are increasingly adopting heterogeneous templates integrating hardware accelerators and application-specific processors, which poses novel challenges. In particular, it is difficult to have accurate control of task activities in Commercial Off-the-shelf (COTS) System on Chips (SoCs), due to complex main memory sharing mechanisms among different computing engines. To address this problem, bandwidth regulation approaches based on monitoring and throttling are widely adopted. Existing solutions, however, are either too coarse-grained, limiting the control over computing engines activities, or platform-dependent, addressing the problem only for specific SoCs. In this paper we propose an innovative, fine-grained and platform-independent approach that can accurately control main memory bandwidth usage in an FPGA-based Heterogeneous System on Chip (HeSoC). Experimental results conducted on the Xilinx Zynq UltraScale+ platform demonstrate that our approach enables solutions not feasible with state-of-the-art bandwidth regulation methods.
Training Accuracy Improvement for ERP Datasets by Employing Restart Learning Strategy

The Event Related Potential (ERP) technique provides a powerful way of exploring brain and neuroscience. However, ERP data are susceptible to different noises and artifacts some of which are impossible to be filtered out. In this case, it is necessary to find a strategy to tolerate these noises, ensuring accuracy even lots of noises showing up. One possible strategy is the restart learning rate schedule (restart schedule) which was developed to deal with the multi-modal optimization problems. In order to verify restart schedule’s effectiveness in improving the accuracy of training ERP data, restart schedule was applied to train different ERP datasets and compared to the other schedules in this paper. The experimental results show that the restart schedule performs the best compared to the other schedules.
A High-Rate Extension to Soundstream

In this paper, we propose a high-rate extension of the SoundStream codec which is able to generate almost transparent quality audio at 16 kbps for wideband speech signals. SoundStream shows reasonably good performance at low bit-rates (e.g. around 9 kbps), but its performance does not improve much when more bits are used for encoding the latent embeddings. Motivated by experimental results showing that neural audio codec performance is highly related to the characteristics of latent embeddings such as dimensionality, dependency, and probability density function shape, we propose a convolutional transformer architecture and an attention-based multi-scale latent decomposition method that significantly enhances codec performance when quantizing high-dimensional embeddings. Experimental results show the superiority of our proposed model over conventional approaches.
A Convolution Neural Network Accelerator Design with Weight Mapping and Pipeline Optimization

The pipeline is an efficient solution to boost performance in non-volatile memory based computing in memory (nvCIM) convolution neural network (CNN) accelerators. However, the previous works seldom focus on pipeline optimization from the perspective of the whole system, especially overlooking the effect of buffer access. In this work, we propose a high-performance NVM-based CNN accelerator with a balanced pipeline design, which takes account of both the macro computing and the buffer access. At the operator level, a matrix-based weight mapping method is proposed to reduce buffer access delay. At the macro level, decoupled access and execution design is introduced to shorten the single-layer latency. At the system level, a hybrid inter/intra-tile design is presented to balance the overall latency across CNN layers. With the collaboration among three methods, we construct a well-balanced pipeline for the nvCIM accelerator at a smaller hardware cost. Experiments show that our pipeline design can achieve 3. 7х, 7. 5х, and 3. 5х throughput improvement for recognition of ImageNet with ResNet18, VGG19, and ResNet34 models, respectively.
ActiWate: Adaptive and Design-agnostic Active Watermarking for IP Ownership in Modern SoCs

Watermarking offers a viable solution to combat IP piracy and illegal re-use. However, watermarking verification techniques rely heavily on manual testing by verification engineers and ignore the possibility of having a rogue SoC design house. To automate the watermarking-based verification process and to be against wider attacks (e.g., rogue design house), this paper presents ActiWate, which conducts automatic self-verification by communicating with various peripherals within the SoC. Showing its resilience against removal and spoofing attacks, ActiWate is architectured to be an IP/SoC-agnostic watermarking and our experiments demonstrate its versatility by implementing it on multiple RISC-V SoCs with different components/peripherals.
Performance analysis of the packet-based PNT service in NGSO broadband satellite communication systems

Providing alternative PNT service to GNSS-challenged users will be an important function of next-generation NGSO broadband satellite communication systems. Herein, a packet-based PNT service architecture in NGSO broadband systems is proposed in which a primary satellite and selected assistant satellites work together to provide PNT service to requesting users. Its positioning performance bounds are mathematically formulated by rigorously analyzing the bounds constrained by different waveforms. Simulations are conducted on different configurations of Walker Delta MEO constellations and Walker Star LEO constellations for corroboration, revealing the following: (1) Both MEO and LEO constellations achieve sub-meter-level positioning precision given enough satellites. (2) Compared to the GNSS Doppler-based velocity estimation method, the position advance based velocity estimation algorithm is more precise and applicable to the PNT service in NGSO broadband systems. (3) To provide PNT service to users in GNSS-challenged environments, the primary and each assistant satellite need only ∼0.1%o of the time of one downlink beam.
Approximation of Solutions to Nonautonomous Difference Equations

Abstract We study the asymptotic properties of solutions to nonautonomous difference equations of the form Δmxn=anf(n,xσ(n))+bn, f:𝕅×𝕉→𝕉, σ:𝕅→𝕅 $${\Delta ^m}{x_n} = {a_n}f(n,{x_{\sigma (n)}}) + {b_n},\,\,f:N \times {\Bbb R} \to {\Bbb R},\,\,\sigma :{\Bbb N} \to {\Bbb N}$$ Using the iterated remainder operator and asymptotic difference pairs we establish some results concerning approximative solutions and approximations of solutions. Our approach allows us to control the degree of approximation.
Late Breaking Results: RL-LPO: Reinforcement Learning Based Leakage Power Optimization Framework with Graph Neural Network

Leakage power optimization based on threshold voltage (Vth) assignment poses great challenge in circuit design due to its tremendous solution space. In this paper, a Reinforcement Learning-based Leakage Power Optimization framework (RL-LPO) is first-ever proposed to formulate Vth assignment as a reinforcement learning (RL) process by learning timing and physical characteristics of each circuit instance with Graph Neural Networks (GNN). The proposed RL-LPO was validated by the IWLS2005 and Opencores benchmark circuits with TSMC 28nm technology and experimental results demonstrate that our work achieves better leakage power optimization by additional 3% reduction on average than the commercial tool PrimeTime with 6.7× speed up when being transferred to unseen circuits with negligible timing degradation.
The Probabilistic Component of Outdoor Millimeter Wave Propagation Path Loss Model Considering Rain Fade

The close-in free space reference distance model CI can be extended to account for the channel shadow fading SF and rain attenuation factors as a different time probability function. Robustness and performance motivated the adoption of combining rain fade and shadowing using the CI model at exceedance probability (0.001% $\le$ P% $\le$ 1.0%) and weighing the path losses using a probabilistic distribution of rain fade shadowing as a function of link distance. A probabilistic CI model is proposed considering rain attenuation and shadowing at different probabilities. The mean estimated path loss in this new "hybrid" path loss model is probabilistic. The model can give a close prediction compared to path loss analytically estimated from measured data at 38 GHz at 300m ($\chi \sigma =$5.22 dB). The difference between path loss predicted from the proposed probabilistic model and path loss analytically estimated from measured path loss at 38 GHz over 300 m at (0.001% $\le$ p % $\le$1.0%) has been calculated at the tropical region. The findings show a 20 dB per decade loss in signal strength in the equatorial region more than in the temperate areas by considering rain fade for 300 m at 38 GHz. The proposed hybrid probabilistic path loss model can be used as an alternative to conventional propagation path loss models to calculate the directional path loss by increasing the prediction accuracy. The effect of lognormal shadowing, which essentially accounts for the randomness in the shadowing factor around the cell because of the large obstacles, has also been analysed. Additional transmit power is proposed to maintain the fade margin during the rains. Probabilistic path loss models are commonly used in the design and evaluation of millimeter wave wireless systems, which operate at high frequencies and are highly sensitive to the propagation environment. By accounting for the probabilistic nature of the path loss, these models can help to improve the accuracy of predictions and reduce the risk of unexpected performance degradation in real-world deployments.
Research on Container Throughput Prediction of Chongqing Port Based on Shipping Network

Port throughput forecasting is an important port development and planning reference index. The container throughput of Chongqing port and its influencing factors are analyzed, and a shipping network-based container throughput forecasting method is proposed. Grey correlation analysis is conducted based on the historical data of container throughput of Chongqing port from 2010 to 2020.BP neural network forecasting method is applied to achieve static forecasts of the container throughput of the Chongqing port. A shipping network centered on Chongqing Port is established based on the container route data of Chongqing Port and surrounding ports. Thus, the dynamic prediction value of the container throughput of Chongqing Port is calculated according to the shipping network circle index. The shipping network-based port container throughput forecasting algorithm could objectively reflect the correction of port throughput by regional development.
Graph Representation Learning for Microarchitecture Design Space Exploration

Design optimization of modern microprocessors is a complex task due to the exponential growth of the design space. This work presents GRL-DSE, an automatic microarchitecture search framework based on graph embeddings. GRL-DSE uses graph representation learning to build a compact and continuous embedding space. Multi-objective Bayesian optimization using an ensemble surrogate model conducts microarchitecture design space exploration in the graph embedding space to efficiently and holistically optimize performance-power-area (PPA) objectives. Experimental studies on RISC-V BOOM show that GRLDSE outperforms previous techniques by 74.59% on Pareto front quality and outperforms manual designs in terms of PPA.
Safety Benefits Evaluation of Mixed Traffic Flow with Connected and Automated Vehicles under Snowy Conditions

Connected and automated vehicles are widely agreed to bring tremendous benefits to the traffic system. But the understanding of them is still challenging due to the complexity of the mixed traffic flow of human-driven (HDVs), automated vehicles (AVs), and connected and automated vehicles (CAVs), and due to our limited knowledge of the impacts of adverse weather conditions. Given this, this study was driven to test the safety performance of the above mixed traffic flow both in sunny and snowy conditions under various market penetration rates (MPRs) using SUMO. Minimum gap, the acceleration/deceleration ability of vehicles, and the drivers’ desired (minimum) time headway were specifically calibrated for car-following behaviors by a Kolmogorov-Smirnov test based on data collected at an intersection in China. Three surrogate safety measures, i.e., time to collision (TTC), Time-Exposed TTC (TET), and brake rate (BR), were utilized to evaluate the real-time crash risk variations. The results show that 1) the safety performance improved in sunny and snowy conditions when the traffic flow was mixed with AVs and CAVs; 2) the improvement of AVs and CAVs in avoiding serious conflicts was more significant in the snowy condition, and 3) the performance of the braking rate was different when MPRs increased in the sunny and snowy conditions. In addition, the difference in safety benefits between AVs and CAVs in sunny and snowy conditions was analyzed. The findings of this study provide valuable insights for managing connected and automated traffic under the snowy condition on urban roads.
Towards a multi-platform assimilative system for North Sea 1 biogeochemistry 2

19 Oceanography has entered an era of new observing platforms, such as biogeochemical 20 Argo ﬂoats and gliders, some of which will provide three-dimensional maps of essential 21 ecosystem variables on the North-West European (NWE) Shelf. In a foreseeable future 22 operational centres will use multi-platform assimilation to integrate those valuable data 23 into ecosystem reanalyses and forecast systems. Here we address some important ques- 24 tions related to glider biogeochemical data assimilation and introduce multi-platform data 25 assimilation in a (pre)operational model of the NWE Shelf-sea ecosystem. We test the 26 impact of the diﬀerent multi-platform system components (glider vs satellite, physical vs 27 biogeochemical) on the simulated biogeochemical variables. To characterize the model 28 performance we focus on the period around the phytoplankton spring bloom, since the 29 bloom is a major ecosystem driver on the NWE Shelf. We found that the timing and mag- 30 nitude of the phytoplankton bloom is insensitive to the physical data assimilation, which is 31 explained in the study. To correct the simulated phytoplankton bloom one needs to assim- 32 ilate chlorophyll observations from glider or satellite Ocean Color (OC) into the model. 33 Although outperformed by the glider chlorophyll assimilation, we show that OC assimila- 34 tion has mostly desirable impact on the sub-surface chlorophyll. Since the OC assimilation 35 updates chlorophyll only in the mixed layer, the impact on the sub-surface chlorophyll is 36 the result of the model dynamical response to the assimilation. We demonstrate that the 37 multi-platform assimilation combines the advantages of its components and always per- 38 forms comparably to its best performing component. 39 to measure three-dimensional distributions of some essential biogeochemical variables on the NWE Shelf. This work establishes a multi- platform assimilative system on the NWE Shelf which will be used to combine multiple 46 diﬀerent types of observing platforms (e.g. satellite, gliders) with our up-to-date models in 47 order to optimize our estimate and forecast of the NWE Shelf ecosystem state. We provide 48 an understanding for how the diﬀerent components of the system interact. We demonstrate 49 that the assimilative system is skilled to combine physical data with satellite and glider 50 data for chlorophyll, as well as the glider data for oxygen. The work establishes the foun- 51 dations of a system that is planned to be used in the future operational oceanography on 52 the NWE Shelf.
Non-equilibrium Field Theory of Condensed Matter Systems - Periodic Driving and Quantum Quenches

Quantum many-body systems far from equilibrium can show a wide range of different phenomena. Understanding non-equilibrium many-body systems is a non-trivial task, as one has to deal with a fully-time dependent situation and interactions. Field theoretic methods can provide insight into non-equilibrium physics. In this thesis we apply conserving approximations to study heating and dynamical quantum phase transitions in the $O(N)$-model, many-body localization of spinless lattice fermions and the dynamical full counting statistics after quenches in a Luttinger liquid.
Ensemble Deep Learning based Real-time Log Anomaly Detection

Applications/Services generate a large amount of logs constantly. It is a crucial task for administrators to extract useful information from logs. Analysis of these logs is essential to find anomalies and fix issues quickly in the application/service. It is a very tedious task to go through all the log messages manually to find anomalies. This points to the need for an anomaly detection system. An anomaly detection system automates the analysis of logs generated by the system to discover abnormalities in the system execution. To build the anomaly detection system, three deep learning models i.e. LSTM (Long short-term memory), BERT (Bidirectional Encoder Representations from Transformers), and CNN (Convolutional neural network) is proposed. Ensemble of these three models using the voting technique is built. The ensemble of deep learning models is done to arrive at an optimal prediction for anomalies as these models compensate for the drawbacks of each other. This paper also talks about the implementation of real-time anomaly detection by leveraging the existing bulk log parser called Drain to do online parsing. A user interface is provided to view the detected anomalies and filter the logs accordingly.
Heterogeneous Reconfigurable Accelerators: Trends and Perspectives

Heterogeneity and reconfigurability have both been adopted by accelerators to improve their flexibility and efficiency for a wide variety of applications, from cloud computing to embedded systems. This paper provides an overview of the trends of heterogeneous reconfigurable accelerators including field-programmable gate arrays and coarse-grained reconfigurable arrays, and the related design automation approaches for enhancing design quality and designer productivity of these accelerators. We shall also discuss how recent advances in technology, such as multi-level co-design, heterogeneous Function-as-a-Service and meta-programming, would help address the challenges in engineering next-generation heterogeneous reconfigurable accelerators and beyond.
An Optimized Snapshot Raft Algorithm for Log Compression

Consensus algorithm is widely used in distributed system, which can solve the problems of fault tolerance and data consistency. Raft algorithm is one of the most widely used consensus algorithms to maintain consensus and fault tolerance in some distributed systems. However, the original Raft algorithm may fail to consider the problems of recovering the logs of all servers from failures as well as the increase number of logs under failures such as node downtime and unreliable networks. To this end, we propose an optimized Raft algorithm based on Snapshot technology, called Snapshot Raft (SS-Raft). Snapshot is one of the methods of log compression. When consensus is reached, the SS-Raft algorithm can exploit the snapshot technique to compress the number of entries submitted to the log. The experimental results illustrate that the number of log entries submitted by the server decrease significantly under unreliable network and churn failures, which will effectively reduce the storage space of nodes and greatly improve the system performance. In addition, the SS-Raft algorithm can also operate normally under a variety of failure environments and improve the performance of distributed systems.
PROPHET: Predictive On-Chip Power Meter in Hardware Accelerator for DNN

On-chip power meters play a critical role in power management by generating timely and accurate power traces at runtime. However, both performance-counter-based and existing RTL-based on-chip power meters have difficulty in providing sufficient response time for fast power and voltage management scenarios. Additionally, they can be costly to implement for large-scale DNN accelerators with many homogeneous process elements. To address these limitations, this paper proposes PROPHET, a data-pattern-based predictive on-chip power meter targeting multiply-accumulate-based DNN accelerators. By sampling pre-defined data patterns during memory access, PROPHET can predict power consumption before it actually happens. In our experiments, PROPHET predicts power consumption dozens of clock cycles in advance, with a temporal resolution of 4 clock cycles and NMAE < 7% and area overhead < 2% for various systolic-array-based DNN accelerators. PROPHET has the potential to enable fine-grained power management and optimization for large-scale DNN accelerators, improving their energy efficiency.
Lexical Semantics

Lexical semantics is concerned with inherent aspects of word meaning and the semantic relations between words, as well as the ways in which word meaning is related to syntactic structure. This chapter provides an introduction to some of the main themes in lexical semantic research, including the nature of the mental lexicon, lexical relations, and the decomposition of words into grammatically relevant semantic features. The mapping between the semantics of verbs and their associated syntax is discussed in terms of thematic roles, semantic structure theory, and feature selection. A review of some of the most influential findings in second language research involving both open-class and closed-class lexical items reveals important implications for classroom pedagogy and syllabus design in the domain of vocabulary instruction.