Central Processing Unit Research Articles

Synaptic plasticity and associative learning in IGZO-based synaptic transistor

The increasing demand for efficient data access in high-performance computing systems has emphasized the limitations of the traditional von Neumann architecture, particularly due to the von Neumann bottleneck. This bottleneck arises from the significant power and time consumption required for data transfer between the central processing units and memory, impeding overall system efficiency. Neuromorphic computing offers a promising alternative. Neuromorphic systems emulate biological neurons and synapses, enabling parallel data processing and potentially overcoming the limitations of traditional computing architectures. This study focuses on synaptic transistors with In–Ga–Zn-O (IGZO) channels and TaOx charge trapping layers (Ox-CTL) to enhance neuromorphic computing capabilities. The devices were fabricated using reactive sputtering and atomic layer deposition, followed by comprehensive structural and compositional analyses. Experimental results demonstrated significant hysteresis, high on/off ratios, and multibit conductance states, suggestive of effective charge trapping and detrapping mechanisms. Evaluation through potentiation, depression, and excitatory postsynaptic current (EPSC) measurements, along with simulations using the Modified National Institute of Standards and Technology (MNIST) database, revealed improved pattern recognition accuracy. Additionally, associative learning experiments modeled after Pavlov’s dog conditioning emphasized the device's capability for both long- and short-term memory retention. These findings suggest that IGZO-based synaptic transistors are promising candidates for next-generation neuromorphic computing systems, offering enhanced data processing efficiency and adaptability.

Design and implementation of a nano-scale high-speed multiplier for signal processing applications

Digital signal processing (DSP) is an engineering field involved with increasing the precision and dependability of digital communications and mathematical processes, including equalization, modulation, demodulation, compression, and decompression, which can be used to produce a signal of the highest caliber. To execute vital tasks in DSP, an essential electronic circuit such as a multiplier plays an important role, continually performing tasks such as the multiplication of two binary numbers. Multiplier is a crucial component utilized to implement a wide range of DSP tasks, including convolution, Fourier transform, discrete wavelet transforms (DWT), filtering and dithering, multimedia information processing, and more. A multiplier device includes a clock and reset buttons for more flexible operational control. Each digital signal processor constitutes a multiplier unit. A multiplier unit functions entirely autonomously from the central processing unit (CPU); consequently, the CPU is burdened with a significantly reduced amount of work. Since DSP algorithms must constantly carry out multiplication tasks, the employment of a high-speed multiplier to execute fast-speed filtering processes is vital. The previous multipliers had lots of weaknesses, such as high energy, low speed, and high area, because they implemented this necessary circuit based on traditional technology such as complementary metal-oxide semiconductor (CMOS) and very large-scale integration (VLSI). To solve all previous drawbacks in this necessary circuit, we can use nanotechnology, which directly affects the performance of the multiplier and can overcome all previous issues. One of the alternative nanotechnologies that can be used for designing digital circuits is quantum dot cellular automata, which is high speed, low area, and low power. Therefore, this manuscript suggests a quantum technology-based multiplier for DSP applications. In addition, some vital circuits, such as half adder, full adder, and ripple carry adder (RCA), are suggested for designing a multiplier. Moreover, a systolic array, accumulator, and multiply and accumulate (MAC) unit are proposed based on the quantum technology-based multiplier. Nonetheless, each of the suggested frameworks has a coplanar configuration without rotated cells. The suggested structure is developed and verified utilizing the QCADesigner 2.0.3 tools. The findings showed that all circuits have no complicated configuration, including a higher number of quantum cells, latency, and an optimum area.

Green Lean Six Sigma practice in the industrial and service sectors: from the systematic literature review to an integration diagram

Recently, increasing competition pressure and severe environmental situations have posed significant challenges to organizations in the industrial and service sectors. In this context, Green Lean Six Sigma (GLSS) is an important approach for helping industrial and service organizations achieve sustainability. The main objective of this study was to clarify the specific content of GLSS practices in the industrial and service sectors, including frameworks, tools, necessities, barriers, effects, and other aspects, through a systematic literature review (SLR). A statistical analysis was conducted to examine the main characteristics of these GLSS elements and the gaps in their implementation between the two sectors. Based on this analysis, an integration diagram of GLSS key elements is proposed, and it showcases core ideas and processes for implementing GLSS in the industrial and service sectors. The findings demonstrate that GLSS practices bring comprehensive improvements to enterprises in multiple dimensions and exhibit differentiated characteristics in the two sectors. Most GLSS frameworks are primarily designed for the industrial sector, and service organizations have yet to systematically grasp the application of GLSS tools. The findings have reference value for practitioners and researchers in the industrial and service sectors regarding GLSS practices.

Планирование эксперимента на модели медицинской организации, реализующей региональный проект «Борьба с сердечно-сосудистыми заболеваниями» для нахождения элемента оптимального прототипа.

Prevention of cardiovascular diseases and complications in high-risk patients is the most important component of the federal project “Combating Cardiovascular Diseases”, aimed at achieving the goals and results of its implementation. The purpose was the identification of factors influencing the processes of the regional project “Fighting Cardiovascular Diseases” (object of research), to find and optimize elements of a prototype model of a medical organization, using experimental planning methods. Materials and methods. The research methods were experimental planning and process modeling to select the number and conditions for setting up the experiment necessary to solve a specific problem with the required accuracy for decision making. Planning the experiment is aimed at trying to construct an optimal model of a medical organization to implement the strategic goals of the federal project. Results. The object of the study was the processes implemented by medical organizations within the framework of the federal project “Combating Cardiovascular Diseases”, which is part of the national project “Healthcare”. The author has identified constant and variable factors influencing the actual model of the medical organization implementing the project to find an element of the optimal prototype within the framework of the experiment planning processes. From the numerous ranges of available factors of influence (processes of “entrance”) on the object of research, organizational processes in the operational core of the organizational structure of the management of a medical organization have become key elements of the study. Finding the maximum optimistic, pessimistic and probable calculated indicators of the load on medical specialists (the “exit” processes) became the criteria (responses) for determining and selecting the optimal time for organizing processes in the management apparatus of the operational core of a medical organization. Conclusion. By optimizing one element of the model of a medical organization implementing a project, it is impossible to fully optimize the prototype, given the multi-level nature of the healthcare system and the complexity of interconnected elements. As part of the study of the research question, it is planned to continue the search for elements of the optimal prototype. Consecutive execution of iterations with different values of the optimization parameters of the elements and finding the optimal values of the prototype will allow achieving the value of the given objective function with the least loss of resources.

Entertainment robots based on smartphone terminals for mobile network music E-learning mode

This study adopted the method of system requirements analysis to design the functions and structure of the network music E-learning mode system on smartphone terminals. Through research and user requirement analysis, the core functions and requirements of the system were determined, and the system was designed in terms of functionality and structure. The system’s functional design adopts speech recognition technology, enabling entertainment robots to accurately understand user voice commands and questions. Through natural language processing and machine learning algorithms, entertainment robots can analyze user needs and recommend suitable music teaching resources based on their interests and learning goals. Divide the system into two parts: front-end and back-end. The front-end is the user interface, and through a user-friendly graphical interface and interactive design, users can easily operate the system and interact with entertainment robots. The backend is the core processing part of the system, which achieves the application of entertainment robots in the network music E-learning mode on smartphone terminals through the collaboration of the front-end and back-end. The entertainment robot based on smartphone terminals has high effectiveness and feasibility in the application of mobile network music E-learning mode. The performance test results of the system indicate that it can run stably and meet the concurrent needs of a large number of users.

Exosomal miR-17-5p derived from epithelial cells is involved in aberrant epithelium-fibroblast crosstalk and induces the development of oral submucosal fibrosis

Oral submucous fibrosis (OSF) is a chronic and inflammatory mucosal disease caused by betel quid chewing, which belongs to oral potentially malignant disorders. Abnormal fibroblast differentiation leading to disordered collagen metabolism is the core process underlying OSF development. The epithelium, which is the first line of defense against the external environment, can convert external signals into pathological signals and participate in the remodeling of the fibrotic microenvironment. However, the specific mechanisms by which the epithelium drives fibroblast differentiation remain unclear. In this study, we found that Arecoline-exposed epithelium communicated with the fibrotic microenvironment by secreting exosomes. MiR-17-5p was encapsulated in epithelial cell-derived exosomes and absorbed by fibroblasts, where it promoted cell secretion, contraction, migration and fibrogenic marker (α-SMA and collagen type I) expression. The underlying molecular mechanism involved miR-17-5p targeting Smad7 and suppressing the degradation of TGF-β receptor 1 (TGFBR1) through the E3 ubiquitination ligase WWP1, thus facilitating downstream TGF-β pathway signaling. Treatment of fibroblasts with an inhibitor of miR-17-5p reversed the contraction and migration phenotypes induced by epithelial-derived exosomes. Exosomal miR-17-5p was confirmed to function as a key regulator of the phenotypic transformation of fibroblasts. In conclusion, we demonstrated that Arecoline triggers aberrant epithelium-fibroblast crosstalk and identified that epithelial cell-derived miR-17-5p mediates fibroblast differentiation through the classical TGF-β fibrotic pathway, which provided a new perspective and strategy for the diagnosis and treatment of OSF.

Dynamic calculation of stress influence law of rubber core sealing surface of the rotating blowout preventer with defects

The bubble and crack defects in the production and operation of the rubber core of the rotating blowout preventer (RBOP) easily cause fatigue failure, leading to well-controlled severe accidents. Based on Yeoh’s principal structure equation, the dynamic sealing model of rubber core was established in this paper. The sealing process of the rubber core in lifting and drilling was calculated and analyzed. The stress influence law of typical cracks and bubble defects that affect the fatigue failure of rubber core sealing surface was analyzed. The results are as follows: (1) When the drill pipe joint passes through the RBOP, the structure of the rubber core is significantly deformed. This stage may cause damage and failure of the rubber core. (2) The cracks and bubble defects lead to a decrease in the stability of the sealing surface. With the increase of stress at the defect, the crack of the rubber core is easy to expand. (3) In the same location of the rubber core defects, cracks compared to bubble defects have a more significant impact. For the same type of crack, the bottom left side (A1) and the outer surface underside (D1) crack reduce the stress at the position of the rubber core sealing surface to the maximum. The research results provide a reference basis for defect detection, sealing performance, and service life of rubber cores, in order to improve the reliability evaluation standards of blowout preventer rubber cores, improve well control safety, reduce the occurrence of safety accidents, and improve operational efficiency.

Person Centred Care in Neurological Rehabilitation: A Case Study and Critical Reflection

Healthcare systems and organizations consider maintaining and improving the quality of their service provisions whilst also being mindful of strategies to mitigate identified gaps in their clinical services. One such approach that has found favour among healthcare teams and institutions is that of person centered care. Person centered care places emphasis on situating the individual at the core of the decision-making process, identifying, and illuminating what is of greatest importance to the concerned person. The approach fosters a belief in equal and collaborative goal setting between the person and their healthcare team, respecting the individual’s needs and their preferences as people. One area where this approach can be adopted is that of neurological rehabilitation. The ethos of mutual respect and partnership between the individual and the multidisciplinary team can guide and inform the process of rehabilitation. This paper aims to deliver a critical appraisal of the application of the principles of person centered care in neurological rehabilitation. This will be referenced to a clinical scenario involving a young person with a stroke. The case will link the theory of person centered care discussing the models and frameworks realised in the clinical story. This will lead to a discussion and conclusion on the value of person centered care for people, professionals, teams, and organizations.

TORO Indexer: a PyTorch-based indexing algorithm for kilohertz serial crystallography.

Serial crystallography (SX) involves combining observations from a very large number of diffraction patterns coming from crystals in random orientations. To compile a complete data set, these patterns must be indexed (i.e. their orientation determined), integrated and merged. Introduced here is TORO (Torch-powered robust optimization) Indexer, a robust and adaptable indexing algorithm developed using the PyTorch framework. TORO is capable of operating on graphics processing units (GPUs), central processing units (CPUs) and other hardware accelerators supported by PyTorch, ensuring compatibility with a wide variety of computational setups. In tests, TORO outpaces existing solutions, indexing thousands of frames per second when running on GPUs, which positions it as an attractive candidate to produce real-time indexing and user feedback. The algorithm streamlines some of the ideas introduced by previous indexers like DIALS real-space grid search [Gildea, Waterman, Parkhurst, Axford, Sutton, Stuart, Sauter, Evans & Winter (2014). Acta Cryst. D70, 2652-2666] and XGandalf [Gevorkov, Yefanov, Barty, White, Mariani, Brehm, Tolstikova, Grigat & Chapman (2019). Acta Cryst. A75, 694-704] and refines them using faster and principled robust optimization techniques which result in a concise code base consisting of less than 500 lines. On the basis of evaluations across four proteins, TORO consistently matches, and in certain instances outperforms, established algorithms such as XGandalf and MOSFLM [Powell (1999). Acta Cryst. D55, 1690-1695], occasionally amplifying the quality of the consolidated data while achieving superior indexing speed. The inherent modularity of TORO and the versatility of PyTorch code bases facilitate its deployment into a wide array of architectures, software platforms and bespoke applications, highlighting its prospective significance in SX.

Insight from industry-early lessons learned about AI adoption in core procurement processes, directions for managers and researchers

PurposeThere is a growing body of conceptual work considering the potential of AI in supply chain and procurement, and there is great interest in AI among managers. But, according to a recent study, digital strategies for procurement are often missing or not satisfactory. Literature offers conflicting guidance on possible adoption areas for AI in core procurement processes. Given the need for better digital strategies for procurement and the need to further develop the understanding of adoption potential, the purpose of this paper is to explore actual adoption levels, experienced benefits, readiness levels and barriers to implementation in industry. This informs nuanced, not hyped, managerial consideration and identifies further research opportunities.Design/methodology/approachLeveraging items used in literature to study adoption of other technologies, the authors conduct the first empirical exploration of actual adoption levels of AI in procurement. The authors do so by collecting survey responses in three manager workshops, and the authors use the workshops to seek manager input in the interpretation of findings and the identification of implications for managers and researchers.FindingsThere appears to be less consideration given to AI in procurement than interest in the topic might imply. Adoption levels are generally low, implying that there is a lot of room for the development of consideration, use cases and possible pilots by managers and researchers. The authors find procurement benefits of AI adoption to be broader than costs and productivity alone, including visibility and innovation. But, readiness appears to be at relatively low levels with factors commonly considered in literature, such as executive support and willingness to invest, less relevant than less widely considered elements such as human sense making and supplier readiness.Originality/valueThis first empirical exploration moves past conceptualization and the study of potential adoption into the study of actual adoption levels in different procurement core processes. The authors expand the consideration of readiness by including additional items of human sense making as called for in literature. The authors also include and develop supplier readiness consideration, which is often missing from research. With the help of participating managers, the authors are able to develop a more comprehensive framework for the consideration of AI adoption. This can help bring nuance, not hype, to consideration and provides a rich portfolio of research items and constructs to further explore.

SpChar: Characterizing the sparse puzzle via decision trees

Sparse matrix computation is crucial in various modern applications, including large-scale graph analytics, deep learning, and recommender systems. The performance of sparse kernels varies greatly depending on the structure of the input matrix, making it difficult to gain a comprehensive understanding of sparse computation and its relationship to inputs, algorithms, and target machine architecture. Despite extensive research on certain sparse kernels, such as Sparse Matrix-Vector Multiplication (SpMV), the overall family of sparse algorithms has yet to be investigated as a whole. This paper introduces SpChar, a workload characterization methodology for general sparse computation. SpChar employs tree-based models to identify the most relevant hardware and input characteristics, starting from hardware and input-related metrics gathered from Performance Monitoring Counters (PMCs) and matrices. Our analysis enables the creation of a characterization loop that facilitates the optimization of sparse computation by mapping the impact of architectural features to inputs and algorithmic choices. We apply SpChar to more than 600 matrices from the SuiteSparse Matrix collection and three state-of-the-art Arm Central Processing Units (CPUs) to determine the critical hardware and software characteristics that affect sparse computation. In our analysis, we determine that the biggest limiting factors for high-performance sparse computation are (1) the latency of the memory system, (2) the pipeline flush overhead resulting from branch misprediction, and (3) the poor reuse of cached elements. Additionally, we propose software and hardware optimizations that designers can implement to create a platform suitable for sparse computation. We then investigate these optimizations using the gem5 simulator to achieve a significant speedup of up to 2.63× compared to a CPU where the optimizations are not applied.

Large-Eddy Simulations of a Supersonic Impinging Jet Using OpenFOAM

Supersonic impinging jets are a versatile configuration that can model the compressible flows of cold-spray manufacturing and vertical take-off-and landing strategy. In this work, rhoCentralFoam, solver of the OpenFOAM framework, and a large-eddy simulation formulation were used to simulate an underexpanded supersonic jet of Mach 1.45 and nozzle pressure ratio of 4, impinging on a flat wall situated at 1.5 nozzle diameters away from the jet outlet. Care was taken in the mesh construction to properly capture the characteristic standoff shock and vortical structures. The grid convergence index was evaluated with three meshes of increasing spatial resolution. All meshes can generally be considered as sufficient in terms of results focused on time-averaged values and mean physical properties such as centerline Mach number profile. However, the highest resolution mesh was found to capture fine shear vortical structures and behaviors that are absent in the coarser cases. Therefore, the notion of adequate grid convergence may differ between analyses of time-averaged and transient information, and so should be determined by the user’s intention for conducting the simulations. To guide the selection of mesh resolution, scaling analyses were performed, for which the current rhoCentralFoam solver displays a good weak scaling performance and maintains a linear strong scaling up to 4096 cores (32 nodes) for an approximately 40 million-cell mesh. Due to the internode communication bottlenecks of OpenFOAM and improvements in central processing units, this work recommends, for future scaling analyses, adopting a “cells-per-node” basis over the conventional “cells-per-core” basis, with particular attention to the interconnect speed and architecture used.

DyUnS: Dynamic and uncertainty-aware task scheduling for multiprocessor embedded systems

In this paper, an uncertainty-aware task scheduling approach capable of dynamically applying on multiprocessor embedded systems called ”DyUnS” is presented. This method is based on a type-2 fuzzy inference system to consider all design challenges of multiprocessor embedded systems along with their unavoidable uncertainty caused by the differences in models and measurements. The proposed method employs a fuzzy inference system to approximate the appropriate assignment of the application’s tasks to processing cores based on a defined rank including the main design challenges of the system including execution time, temperature, power consumption, and reliability. Moreover, an uncertainty level is defined for various design challenges as the footprint of uncertainty during the scheduling process to tackle the existing inaccuracy between the static models and dynamic environment. Thus, the generated uncertainty-aware solution could be efficiently employed as a dynamic scheduling at runtime. To demonstrate the effectiveness of DyUnS in tolerating uncertainty, several experiments on various application graphs are performed and its effectually is compared to related studies. Based on these experiments, DyUnS jointly optimizes the main design parameters, and its generated solution could be employed dynamically without violating the system’s thresholds. Moreover, its average difference compared to Monte Carlo uncertainty analysis is about 0.2 for all design parameters in three levels of uncertainty.

Histological tissue classification with a novel statistical filter-based convolutional neural network.

Deep networks have been of considerable interest in literature and have enabled the solution of recent real-world applications. Due to filters that offer feature extraction, Convolutional Neural Network (CNN) is recognized as an accurate, efficient and trustworthy deep learning technique for the solution of image-based challenges. The high-performing CNNs are computationally demanding even if they produce good results in a variety of applications. This is because a large number of parameters limit their ability to be reused on central processing units with low performance. To address these limitations, we suggest a novel statistical filter-based CNN (HistStatCNN) for image classification. The convolution kernels of the designed CNN model were initialized by continuous statistical methods. The performance of the proposed filter initialization approach was evaluated on a novel histological dataset and various histopathological benchmark datasets. To prove the efficiency of statistical filters, three unique parameter sets and a mixed parameter set of statistical filters were applied to the designed CNN model for the classification task. According to the results, the accuracy of GoogleNet, ResNet18, ResNet50 and ResNet101 models were 85.56%, 85.24%, 83.59% and 83.79%, respectively. The accuracy was improved by 87.13% by HistStatCNN for the histological data classification task. Moreover, the performance of the proposed filter generation approach was proved by testing on various histopathological benchmark datasets, increasing average accuracy rates. Experimental results validate that the proposed statistical filters enhance the performance of the network with more simple CNN models.

Artificial intelligence, robotics, and automation viewed through the context of the previous four decades.

Computers and applications of computers into our world have changed dramatically during the past five decades, from early days of minimal central processing unit capacity, limited memory and without advantage of global networking. In this article, the author highlights the application of predictive artificial intelligence in use globally over the last 40 years in process industries. It discusses the novel application of process automation and robotics in health clinical high-volume laboratory use that began as a Canadian innovation initiative and followed by similar innovation extending to other countries.

An Improved Onboard Adaptive Aero-Engine Model Based on an Enhanced Neural Network and Linear Parameter Variance for Parameter Prediction

Achieving measurable and unmeasurable parameter prediction is the key process in model-based control, for which an accurate onboard model is the most important part. However, neither nonlinear models like component level models or LPV models, nor linear models like state–space models can fully meet the requirements. Hence, an original ENN-LPV linearization strategy is proposed to achieve the online modelling of the state–space model. A special network structure that has the same format as the state–space model’s calculation was applied to establish the state–space model. Importantly, the network’s modelling ability was improved through applying multiple activation functions in the single hidden layer and an experience pool that records data of past sampling instants, which strengthens the ability to capture the engine’s strongly nonlinear dynamics. Furthermore, an adaptive model, consisting of a component-level model with adaptive factors, a linear Kalman filter, a predictive model, an experience pool, and two ENN-LPV networks, was developed using the proposed linearization strategy as the core process to continuously update the Kalman filter and the predictive model. Simulations showed that the state space model built using the ENN-LPV linearization strategy had a better model identification ability in comparison with the model built using the OSELM-LPV linearization strategy, and the maximum output error between the ENN-LPV model and the simulated engine was 0.1774%. In addition, based on the ENN-LPV linearization strategy, the adaptive model was able to make accurate predictions of unmeasurable performance parameters such as thrust and high-pressure turbine inlet temperature, with a maximum prediction error within 0.5%. Thus, the effectiveness and the advantages of the proposed method are demonstrated.

Design and dynamic analysis of the scissor deployable mechanism used in the drill for replicating ice cores from the borehole wall

The deep ice cores preserved within the polar ice sheets hold a wealth of valuable information, and the demand for ice cores in related scientific research is on the rise. Thus, the replication of ice cores from the borehole wall holds significant importance. However, current methods face limitations such as difficulties in whipstock retrieval, extended auxiliary working hours, structural complexity, and the necessity of ice core depth calibration. To address this, this study proposes a novel method for replicating ice cores from the borehole wall utilizing a thermal coring drill bit. The design and dynamic analysis of the scissor deployable mechanism employed to drive the drill bit movement were thoroughly examined through theoretical and simulation studies. The motion characteristics of the drill bit during the replication coring process and the stress–strain behavior of the scissor rods were determined and validated through drill bit movement experiments, demonstrating a maximum error of 10%. The findings reveal that the mechanism can meet the strength requirements across three operating stages: horizontal drilling, vertical coring, and ice core recovery, with maximum loads of approximately 284 N, 749 N, and 970 N, respectively. Increasing the thickness of the scissor rods and reducing their length can augment the load-bearing capacity of the structure. Additionally, the speed transmission characteristics derived from the scissor mechanism can offer theoretical support for the drill's control system.

VSP wavefields separating method based on parallel local slope scanning

Due to the detectors being distributed in the target medium, vertical seismic profile (VSP) is a seismic observation method that has the advantages of high resolution and a high signal-to-noise ratio. Separating the mixed wavefields into upgoing and downgoing waves can obtain more obvious dynamic and kinematic characteristics of seismic waves, guiding subsequent imaging and interpretation. The traditional method is mainly based on the pickup of first breaks. Flattening the global seismic data by first breaks can enhance the seismic events through techniques like median filter and singular value decomposition (SVD). However, this method relies on high-precision pickup of first breaks and is limited by zero offset. To address this limitation, we introduce an improved median filtering separation method. This method employs separations of the local dip angles into positive and negative through multi-window scanning (MWS). Due to the high accuracy and robustness of this method in 2-D VSP data, we propose to use the local dip angle of the wavefields to median filter the wavefield through positive and negative angles to obtain upgoing and downgoing waves. This method for wavefield separation is optimized by iteratively identifying the directions of the seismic data. However, this optimization comes at the cost of increased computational requirements, especially under high-precision conditions. To help alleviate this problem, we use multi-thread parallel computing technique on a multi-core central processing unit (CPU) to improve computational efficiency. Finally, we validate the proposed method by testing it on synthetic seismic data and field VSP data, respectively. The results show that this wavefield separation method has advantages in terms of accuracy and robustness compared to the median filtering method based on the first break picking.

Correction of the calculation method of CT state energy in ITIC and Y6 acceptor systems

The charge transfer (CT) state at the donor (D)/acceptor (A) interface in organic solar cell (OSC) is the core of exciton dissociation and geminate recombination processes, which are affected by geometries and electron transition. Recently, the CT mechanism developed by fullerenes has produced some unsuitable phenomena in the application of high performance NFA materials, due to the transformation of the highly (three dimensional) symmetric structure to a linear structure. In order to improve the calculation accuracy and custom-matched calculation models for NFA, two benzo[1,2-b:4,5-b′]difuran (BDF) polymers (P-FT)/3,9-bis(2-methylene-((3-(1,1-dicyanomethylene)-6/7-methyl)-indanone))-5,5,11,11-tetrakis(4-hexylphenyl)-dithieno[2,3-d:2′,3′-d’]-s-indaceno[1,2-b:5,6-b’]dithiophene (m-ITIC) and P-FT/2,2′-((2Z,2′Z)-((12,13-bis(2-ethylhexyl)-3,9-diundecyl-12,13-dihydro-[1,2,5]thiadiazolo[3,4-e]thieno[2″,3″:4′,5′]thieno[2′,3′:4,5]pyrrolo[3,2-g]thieno[2′,3′:4,5]thieno[3,2-b]indole-2,10-diyl) bis (methanylylidene))bis(5,6-difluoro-3-oxo-2,3-dihydro-1H-indene-2,1-diylidene))dimalononitrile (Y6) were selected as the research objects, the four levels with gradient variation were constructed, including identification methods (CDD or NTO), range-separated hybrid (RSH) functionals, polarizable continuum models (PCM) and related ω parameter. The results show that RSH functional is the dominant factor affecting CT state. The ωB97XD functional should be selected without distinguishing CT state types. However, when considering the types and high-energy of CT states, it should be noted that there is a strong system dependence. In addition, if a solvent model needs to be added, solvent optimization should be performed on the ω parameter. This work aims to define uniform ECT calculation models and standards to improve accuracy and provide suitable target selection for relevant research.

Lightweight Hardware-Based Cache Side-Channel Attack Detection for Edge Devices (Edge-CaSCADe)

Cache Side-Channel Attacks (CSCAs) have been haunting most processor architectures for decades now. Existing approaches to mitigation of such attacks have certain drawbacks, namely software mishandling, performance overhead, and low throughput due to false alarms. Hence, “mitigation only when detected” should be the approach to minimize the effects of such drawbacks. We propose a novel methodology of fine-grained detection of timing-based CSCA using a hardware-based detection module. We discuss the design, implementation, and use of our proposed detection module in processor architectures. Our approach successfully detects attacks that flush secret victim information from cache memory like Flush+Reload, Flush+Flush, Prime+Probe, Evict+Probe, and Prime+Abort, commonly known as cache timing attacks. Detection is on time with minimal performance overhead. The parameterizable number of counters used in our module allows detection of multiple attacks on multiple sensitive locations simultaneously. The fine-grained nature ensures negligible false alarms, severely reducing the need for any unnecessary mitigation. The proposed work is evaluated by synthesizing the entire detection algorithm as an attack detection block, Edge-CaSCADe, in a RISC-V processor as a target example. The detection results are checked under different workload conditions with respect to the number of attackers and the number of victims having RSA-, AES-, and ECC-based encryption schemes like ECIES, and on benchmark applications like MiBench and Embench. More than 98% detection accuracy within 2% of the beginning of an attack can be achieved with negligible false alarms. The detection module has an area and power overhead of 0.9% to 2% and 1% to 2.1% for the targeted RISC-V processor core without cache for one to five counters, respectively. The detection module does not affect the processor critical path and hence has no impact on its maximum operating frequency.