Miracle-dz/UniFAR-FTUs huggingface dataset:
Accurate timing prediction at placement stage with look-ahead RC network

Timing closure is a critical but effort-taking task in VLSI designs. In placement stage, a fast and accurate net delay estimator is highly desirable to guide the timing optimization prior to routing, and thus reduce the timing pessimism and shorten the design turn-around time. To handle the timing uncertainty at the placement stage, we propose a fast net delay timing predictor based on machine learning, which extract the fully timing features using a look-ahead RC network. Experimental results show that the proposed timing predictor has achieved average correlation over 0.99 with the post-routing sign-off timing results obtained in Synopsys PrimeTime.
A cross-layer approach to cognitive computing: invited

Remarkable advances in machine learning and artificial intelligence have been made in various domains, achieving near-human performance in a plethora of cognitive tasks including vision, speech and natural language processing. However, implementations of such cognitive algorithms in conventional "von-Neumann" architectures are orders of magnitude more area and power expensive than the biological brain. Therefore, it is imperative to search for fundamentally new approaches so that the improvement in computing performance and efficiency can keep up with the exponential growth of the AI computational demand. In this article, we present a cross-layer approach to the exploration of new paradigms in cognitive computing. This effort spans new learning algorithms inspired from biological information processing principles, network architectures best suited for such algorithms, and neuromorphic hardware substrates such as computing-in-memory fabrics in order to build intelligent machines that can achieve orders of improvement in energy efficiency at cognitive processing. We argue that such cross-layer innovations in cognitive computing are well-poised to enable a new wave of autonomous intelligence across the computing spectrum, from resource-constrained IoT devices to the cloud.
In-situ self-powered intelligent vision system with inference-adaptive energy scheduling for BNN-based always-on perception

This paper proposes an in-situ self-powered BNN-based intelligent visual perception system that harvests light energy utilizing the indispensable image sensor itself. The harvested energy is allocated to the low-power BNN computation modules layer by layer, adopting a light-weighted duty-cycling-based energy scheduler. A software-hardware co-design method, which exploits the layer-wise error tolerance of BNN as well as the computing-error and energy consumption characteristics of the computation circuit, is proposed to determine the parameters of the energy scheduler, achieving high energy efficiency for self-powered BNN inference. Simulation results show that with the proposed inference-adaptive energy scheduling method, self-powered MNIST classification task can be performed at a frame rate of 4 fps if the harvesting power is 1μW, while guaranteeing at least 90% inference accuracy using binary LeNet-5 network.
Unicorn: a multicore neuromorphic processor with flexible fan-in and unconstrained fan-out for neurons

Neuromorphic processor is popular due to its high energy efficiency for spatio-temporal applications. However, when running the spiking neural network (SNN) topologies with the ever-growing scale, existing neuromorphic architectures face challenges due to their restrictions on neuron fan-in and fan-out. This paper proposes Unicorn, a multicore neuromorphic processor with a spike train sliding multicasting mechanism (STSM) and neuron merging mechanism (NMM) to support unconstrained fan-out and flexible fan-in of neurons. Unicorn supports 36K neurons and 45M synapses and thus supports a variety of neuromorphic applications. The peak performance and energy efficiency of Unicorn reach 36TSOPS and 424GSOPS/W respectively. Experimental results show that Unicorn can achieve 2×-5.5× energy reduction over the state-of-the-art neuromorphic processor when running an SNN with a relatively large fan-out and fan-in.
HDPG: hyperdimensional policy-based reinforcement learning for continuous control

Traditional robot control or more general continuous control tasks often rely on carefully hand-crafted classic control methods. These models often lack the self-learning adaptability and intelligence to achieve human-level control. On the other hand, recent advancements in Reinforcement Learning (RL) present algorithms that have the capability of human-like learning. The integration of Deep Neural Networks (DNN) and RL thereby enables autonomous learning in robot control tasks. However, DNN-based RL brings both high-quality learning and high computation cost, which is no longer ideal for currently fast-growing edge computing scenarios. In this paper, we introduce HDPG, a highly-efficient policy-based RL algorithm using Hyperdimensional Computing. Hyperdimensional computing is a lightweight brain-inspired learning methodology; its holistic representation of information leads to a well-defined set of hardware-friendly high-dimensional operations. Our HDPG fully exploits the efficient HDC for high-quality state value approximation and policy gradient update. In our experiments, we use HDPG for robotics tasks with continuous action space and achieve significantly higher rewards than DNN-based RL. Our evaluation also shows that HDPG achieves 4.7× faster and 5.3× higher energy efficiency than DNN-based RL running on embedded FPGA.
libcrpm: improving the checkpoint performance of NVM

libcrpm is a new programming library to improve the checkpoint performance for applications running in NVM. It proposes the failure-atomic differential checkpointing protocol, which addresses two problems simultaneously that exist in the current NVM-based checkpoint-recovery libraries: (1) high write amplification when page-granularity incremental checkpointing is used, and (2) high persistence costs from excessive memory fence instructions when fine-grained undo-log or copy-on-write is used. Evaluation results show that libcrpm reduces the checkpoint overhead in realistic workloads. For MPI-based parallel applications such as LULESH, the checkpoint overhead of libcrpm is only 44.78% of FTI, an application-level checkpoint-recovery library.
Xplace: an extremely fast and extensible global placement framework

Placement serves as a fundamental step in VLSI physical design. Recently, GPU-based global placer DREAMPlace[1] demonstrated its superiority over CPU-based global placers. In this work, we develop an extremely fast GPU accelerated global placer Xplace which achieves around 2x speedup with better solution quality compared to DREAMPlace. We also plug a novel Fourier neural network into Xplace as an extension to further improve the solution quality. We believe this work not only proposes a new, fast, extensible placement framework but also illustrates a possibility to incorporate a neural network component into a GPU accelerated analytical placer.
Hierarchical clustering as an unsurpervised machine learning algorithm for hyperspectral image segmentation of films

In recent years, hyperspectral imaging has been increasingly applied to cultural heritage analysis, conservation, as well as digital restoration practice. However, the processing of such a large amount of data acquired by the instrument remains a challenging problem. In the absence of a machine learning pipeline, the segmentation task conducted in conventional methods is usually time-consuming and labor-intensive or requires pre-knowledge of the chemical nature of the art objects. In this paper, we propose to use the hierarchical clustering algorithm (HCA) as an alternative and necessary machine learning approach to segment the spectral images into clusters with different hierarchies, to maximize the information we can obtain from the high-dimensional hyperspectral data. Data were acquired using a custom-made push-broom VNIR hyperspectral camera (380 – 780 nm) and the materials used were a set of cinematic film samples with different degradation degrees. As preliminary results, different fading areas in the entire dataset are successfully classified and segmented. With the help of the automating and unsupervised algorithm, the effective segmentation of those degradation areas could be beneficial as it provides the basis for the future digital unfading treatment.
NobLSM: an LSM-tree with non-blocking writes for SSDs

Solid-state drives (SSDs) are gaining popularity. Meanwhile, key-value stores built on log-structured merge-tree (LSM-tree) are widely deployed for data management. LSM-tree frequently calls syncs to persist newly-generated files for crash consistency. The blocking syncs are costly for performance. We revisit the necessity of syncs for LSM-tree. We find that Ext4 journaling embraces asynchronous commits to implicitly persist files. Hence, we design NobLSM that makes LSM-tree and Ext4 cooperate to substitute most syncs with non-blocking asynchronous commits, without losing consistency. Experiments show that NobLSM significantly outperforms state-of-the-art LSM-trees with higher throughput on an ordinary SSD.
Strength and Durability Studies on GGBS Concrete

Concrete is probably the most extensively used construction material in the world with about six billion tones being produced every year. It is only next to water in terms of per-capita consumption. However, environmental sustainability is at stake both in terms of damage caused by the extraction of raw material and CO2 emission during cement manufacture. This brought pressures on researchers for the reduction of cement consumption by partial replacement of cement by supplementary materials. These materials may be naturally occurring, industrial wastes or by-products that are less energy intensive. These materials (called pozzalonas) when combined with calcium hydroxide, exhibits cementetious properties. Most commonly used pozzalonas are fly ash, silica fume, metakaolin, ground granulated blast furnace slag (GGBS). This needs to examine the admixtures performance when blended with concrete so as to ensure a reduced life cycle cost. The present paper focuses on investigating characteristics of M20 and M40 grade concrete with partial replacement of cement with ground granulated blast furnace slag (GGBS) by replacing cement via 30%, 40%, 50%. The cubes, cylinders and prisms are tested for compressive strength, split tensile strength, flexural strength. Durability studies with sulphuric acid and hydrochloric acid were also conducted.
Re-thinking Transformer based educational resource recommendation engines for higher education

: Digital Study Assistant (DSA) systems for higher education seek to support learners in identifying, structuring and pursuing their personal educational goals. One strategy to achieve this is to galvanize learner interest in engaging with educational resource beyond the scope of their known, pre-determined curriculum. For this purpose, DSA systems may provide a recommendation engine that matches learner interests in natural language to an educational resource covering the topic of interest. To offer a rich assortment of educational resources, these resources need to be fetched from multiple sources such as MOOC and OER repositories or from the learning management system of a local University. In a previous publication, we have presented SidBERT, a BERT-based natural language processing neural network for educational resource classification and recommendation which has been in active use in a prototypical Digital Study Assistant system. This work seeks to follow up on the SidBERT architecture, by introducing an evolution of SidBERT, SemBERT, that is capable of comparing educational resources on a more fine-grained level, thereby addressing multiple shortcomings of the SidBERT architecture and its application within the DSA software. We present network architecture, training parameters and evaluate SemBERT on two datasets. We compare SemBERT to SidBERT and discuss the implications of SemBERT for DSA systems at large.
Data Processing and Information Retrieval of Atmospheric Measurements

The product of this research work takes raw atmospheric science data as input, and generate clean, standardized, and redundancy-free data as output. There are two major tasks involved for the work: data processing and information retrieval. Data processing involves removing inaccuracies and resolving inconsistencies among data. Information retrieval involves identifying the information needed, extracting the data, and consolidating similar entries. Given the complexity of the process, various techniques have been used in the development. In particular, fuzzy matching and fuzzy rule-based inference engine have been used for removing inconsistencies among data entries, retrieving information from certain sections of data files, and consolidating information from different sources. A rule-based forward chaining system is chosen to represent the factors that associate with the type of measurement as well as their interrelationships, and then make decisions on the category of the measurement. The retrieval of instrument information is aided by a preprocessor based on the natural language processing (NLP) technique. The output of NLP is used to match with an entry in the instrument dictionary using a fuzzy rule-based system to determine the instrument type. A software package based on the algorithms presented in this paper has been developed using a single programming language; the package has been deployed for real-world applications.
Regenerated Chirped Grating-Michelson Interferometer as a Laser Beam Intensity Profiler for CO2 Laser

In this paper, we demonstrate the use of regenerated chirped grating (RCG)-Michelson interferometer for the measurement of laser beam intensity profile. When the grating is exposed to CO2 laser irradiation, heat is generated in the fiber due to the absorption of the laser by the glass fiber. The RCG is manufactured from an 18-mm long seed chirped grating through a thermal annealing treatment, to enhance its thermal resistance and durability against the high temperature induced by the CO2 laser irradiation. The intensity profile of the incident laser on the grating can be determined from the phase shift derivative function calculated from the output spectra of the interferometer. By scanning the grating across the laser beam, a 2-D intensity profile of the CO2 laser can be attained.
Energy-Efficient Short-Long Range Communication Network Combining LoRa and Low-Power Radio for Large-Scale IoT Applications

Energy consumption presents a key issue in Wireless Sensor Networks (WSNs) leading to a significant need for efficient-aware communication technologies. The IEEE 802.15. 4 standard is one of the most used communication schemes, which provides low-cost and low-power transceivers. However, it offers only a few tens of meters as a communication range, which makes it difficult to cover a large-area network of several kilometers. To solve that, wireless sensor nodes with long-range radio and high-power transmission can be used. To overcome the trade-off between the power consumption and communication range, an energy-efficient platform is designed, which embeds long-range communication radio with low-power short-range communication radio. Thereby, a low-power radio CC1101 is combined with a high-power radio RFM95. The first radio is used to communicate the LoRa module to the low power wireless sensor node, panStamp. The second radio aims to connect the designed interface with the LoRa gateway ensuring long-range communication. With this heterogeneous architecture, the experimental validation shows a good performance in terms of energy consumption and battery lifetime. Therefore, the designed long-range and short-range wireless platform can be active maximum of around half a year with an alkaline battery having 1150 mAh as capacity.
A Hybrid Measurement System for Hand Signs Recognition based on EMG-FMG Measurements

Human-machine interaction, precision robotic control, and guide advice for the rehabilitation process are scorching topics. For these topics, detecting and accurately recognizing muscle signals for specific gestures is a proven process, and component. In this direction, this paper designs a hybrid measurement system based on EMG and FMG with an 8-channel FMG (Force Myography) band based on nanocomposite pressure sensors and 4-channel EMG (Electromyography) based on the Myoware chip. To construct the database, we selected three healthy right-handed individuals as subjects. Each individual is asked to perform 13 basic hand gestures, and each gesture is repeated 20 times. A linear support vector machine learns the data for each test subject for classification. The system shows high robustness to one subject measurements, where the minimum archived accuracy between the three volunteers is 99 %. In addition, the system is tested against person independence, where all data of all subjects are mixed. The system achieves 98.1 % accuracy.
Use of an automated guided vehicle as a telepresence system with measurement support

This paper proposes an AGV-based telepresence platform for scientific research purposes. The black box nature of many commercially available telepresence systems makes it difficult for researchers to gather unfiltered and reliable data about the behavior and impressions in telepresence operations. The proposed system aims to close this gap by using a fully accessible architecture using a self-developed, fully self-sufficient AGV, which is equipped with two independent sensor systems for self-localization alongside a 360° camera for a natural use of the telepresence system. The accessibility of different movement features ensures the possibility to tailor the setup to different research questions by restricting the user’s access to any given feature. At the same time, access to all raw and filtered sensor data guarantees the reliability and quality of the acquired data at any given time of the experiment. During the entire time of use, the AGV creates a digital twin of itself, its behavior and its surroundings to allow the user precise comparisons between the expected scenario and the real situation. Due to the modular approach of the platform, both from the physical and software architecture, the proposed design can be further extended to tackle questions outside of its current scope, like providing additional information to the recipient of the system or possible on site interactions with it.
A Flexible Load-Independent Multi-Output Wireless Power Transfer System Based on Cascaded Double T-Resonant Circuits: Analysis, Design and Experimental Verification

There are practical applications requiring the wireless power transfer (WPT) system to supply multiple output voltages to a variety of loads. In this paper, in order to achieve this goal, multiple double T resonant circuits are connected to the common port of the receiver side. Based on the modeling and analysis of the double T resonant circuit, it is found that the load-independent output and input zero phase angle (ZPA) can be derived. The proposed WPT system presents the following merits: 1) output voltages are load independent and can be controlled synchronously against the disturbance from input DC source and mutual coupling by using only one closed loop; 2) the output voltages can be adjusted individually to different levels by configuring circuit parameters; 3) only passive components are used to achieve multiple outputs. The power loss can be reduced due to the parasitic resistance of the passive component is very small and can be negligible; and 4) the reactive power losses are minimized due to the input ZPA of the system. An experimental prototype with two output channels is built, based on which the load independent and the controllable characteristics of the output voltages are verified.
Palmar Dermatoglyphic Patterns In Normal And Congenital Heart Diseased Children

Objective: To evaluate and compare the finger and palmar dermatoglyphic patterns in children with congenital heart diseases with that of the normal healthy children. Study Design: Case control study Methodology: One hundred children with both male and female congenital cardiac diseases. Dermatoglyphic palm and finger patterns were investigated. Results: Ventricular septal defects (VSD) are the most common congenital heart condition in both males (50%) and females (34.8%), followed by atrial septal defects (ASD) in both males (20.4%) and females (20.4%). (39.1%). The majority of loops are located in both the cases and controls groups, followed by whorls, and then arches. With a p-value of 0.001, the rise in whorls in congenital heart diseases is statistically important. When the cases were compared to the controls, the Whorls (p 0.001) and loops (p 0.001) were found to be statistically important, indicating that the Whorls are more common in congenital heart disorders than the controls. Conclusion: Congenital cardiac illnesses are linked to anomalous dermatoglyphic patterns. In congenital children with heart disease, there is an increase in Whorls and a reduction in the number of arches.
Subgraph matching based reference placement for PCB designs: late breaking results

Reference placement is promising to handle the increasing complexity in PCB design. We model the netlist into a graph and use a subgraph matching algorithm to find the isomorphism of the placed template in component combination to reuse the placement. The state-of-the-art VF3 algorithm can achieve high matching accuracy while suffering from high computation time in large-scale instances. Thus, we propose the D2BS algorithm to guarantee matching quality and efficiency. We build and filter the candidate set (CS) according to designed features to construct the CS structure. In the CS optimization, a graph diversity tolerance strategy is adopted to achieve inexact matching. Then, hierarchical match is developed to search the template embeddings in the CS structure guided by branch backtracking and matched nodes snatching. Experimental results show that D2BS outperforms VF3 in accuracy and runtime, achieving 100% accuracy on PCB instances.
Impact of traverse speed during joining of CDA101 plates using FSW process

This study conducts an experimental investigation to explore the impact of the traverse speed of a tool on the tensile strength and micro-structural peculiarities of joints attained during friction stir welding of Cu alloy namely CDA 101 (cid:13)at plates. In this process, other parameters including spinning speed of tool (1100 rpm) and downward force (6 kN) were kept constant. A tool with a cylindrical tapered pin geometry was made to traverse at varying speeds, from 20 mm/min to 45 mm/min. It was observed that the CDA 101 joints fabricated at 20 mm/min were entirely free of (cid:13)aws, while the joints fabricated at other traverse speeds of the tool were featured several weld (cid:13)aws. Grains in the center of the stir zone of the joints obtained at 20 mm/min were uniformly distributed and homogeneous due to the signi(cid:12)cant volume of frictional heat and su(cid:14)cient stirring force. The highest tensile strength of 200.65 MPa (nearly 85.38% of base metal) was exhibited by the joint attained at 20 mm/min.