CPU Unit Research Articles

Research on High-Performance Fourier Transform Algorithms Based on the NPU

Backpack computers require powerful, intelligent computing capabilities for field wearables while taking energy consumption into careful consideration. A recommended solution for this demand is the CPU + NPU-based SoC. In many wearable intelligence applications, the Fourier Transform is an essential, computationally intensive preprocessing task. However, due to the unique structure of the NPU, the conventional Fourier Transform algorithms cannot be applied directly to it. This paper proposes two NPU-accelerated Fourier Transform algorithms that leverage the unique hardware structure of the NPU and provides three implementations of those algorithms, namely MM-2DFT, MV-2FFTm, and MV-2FFTv. Then, we benchmarked the speed and energy efficiency of our algorithms for the gray image edge filtering task on the Huawei Atlas200I-DK-A2 development kits against the Cooley-Tukey algorithm running on CPU and GPU platforms. The experiment results reveal MM-2DFT outperforms OpenCL-based FFT on NVIDIA Tegra X2 GPU for small input sizes, with a 4- to 8-time speedup. As the input image resolution exceeds 2048, MV-2FFTv approaches GPU computation speed. Additionally, two scenarios were tested and analyzed for energy efficiency, revealing that cube units of the NPU are more energy efficient. The vector and CPU units are better suited for sparse matrix multiplication and small-scale inputs, respectively.

Optimized Implementation of Argon2 Utilizing the Graphics Processing Unit

In modern information technology systems, secure storage and transmission of personal and sensitive data are recognized as important tasks. These requirements are achieved through secure and robust encryption methods. Argon2 is an advanced cryptographic algorithm that emerged as the winner in the Password Hashing Competition (PHC), offering a concrete and secure measure. Argon2 also provides a secure mechanism against side-channel attacks and cracking attacks using parallel processing (e.g., GPU). In this paper, we analyze the existing GPU-based implementation of the Argon2 algorithm and further optimize the implementation by improving the performance of the hashing function during the computation process. The proposed method focuses on enhancing performance by distributing tasks between CPU and GPU units, reducing the data transfer cost for efficient GPU-based parallel processing. By shifting several stages from the CPU to the GPU, the data transfer cost is significantly reduced, resulting in faster processing times, particularly when handling a larger number of passwords and higher levels of parallelism. Additionally, we optimize the utilization of the GPU’s shared memory, which enhances memory access speed, especially in the computation of the hash value generation process. Furthermore, we leverage the parallel processing capabilities of the GPU to perform efficient brute-force attacks. By computing the H function on the GPU, the proposed implementation can generate initial blocks for multiple inputs in a single operation, making brute-force attacks in an efficient way. The proposed implementation outperforms existing methods, especially when processing a larger number of passwords and operating at higher levels of parallelism.

A Survey of Universal Quantum von Neumann Architecture.

The existence of universal quantum computers has been theoretically well established. However, building up a real quantum computer system not only relies on the theory of universality, but also needs methods to satisfy requirements on other features, such as programmability, modularity, scalability, etc. To this end, here we study the recently proposed model of quantum von Neumann architecture by putting it in a practical and broader setting, namely, the hierarchical design of a computer system. We analyze the structures of quantum CPU and quantum control units and draw their connections with computational advantages. We also point out that a recent demonstration of our model would require less than 20 qubits.

Massively Parallel Modeling of Battery Energy Storage Systems for AC/DC Grid High-Performance Transient Simulation

Extensive integration of power electronics apparatuses complicates the modern power grid and consequently necessitates time-domain transients study for its planning and operation. In this work, a heterogeneous computing architecture utilizing the CPU and graphics processing unit (GPU) is proposed for the efficient study of interactions between a power grid network and massive utility-scale battery energy storage systems (BESSs). The device-level electromagnetic transient (EMT) simulation aiming at enhanced fidelity of the BESS is conducted simultaneously with electro-mechanical transient stability (TS) simulation which suffices system-level dynamic security assessment. Since the reservation of a large amount of energy storage units is computationally intensive for the CPU, the concurrent multi-streaming, multi-threading capability of GPU is exploited to achieve asynchronous sequential-parallel processing, so that the proposed EMT-TS co-simulation can flexibly harness all available computing resources. The multi-rate scheme is adopted for further computational burden alleviation in addition to achieving timely information exchange. It shows that the heterogeneous computation of an IEEE 118-bus system integrated with a substantial number of distributed batteries becomes feasible following the achievement of a remarkable speedup of over 200, and the device- as well as system-level accuracy are validated by MATLAB/Simulink^® and DSATools^TM/TSAT simulation, respectively.

A deep learning-based mobile application for tree species mapping in RGB images

Tree species mapping is an important type of information demanded in different study fields. However, this task can be expensive and time-consuming, making it difficult to monitor extensive areas. Hence, automatic methods are required to optimize tree species mapping. Here, we propose a deep learning-based mobile application tool for tree species classification in high-spatial-resolution RGB images. Several deep learning architectures were evaluated, including mobile networks and traditional models. A total of 2,349 images were used, of which 1,174 images consisted of the Dipteryx alata species and 1,175 images of other local species. These images were manually annotated and randomly divided into training (70%), validation (20%), and testing (10%) subsets, considering the five-fold cross-validation. We evaluated the accuracy and speed (GPU and CPU) of all the implemented deep learning architectures. We found out that the traditional networks have the best performance in terms of F1 score; however, mobile networks are faster. Inception V3 model achieved the best accuracy (F1 score of 97.4%), and MobileNet the worst (F1 score of 83.84%). The MobileNet obtained the best classification speed for CPU (with a mean execution time of 102.8 ms) and GPU (72.4 ms) units. For comparison, Inception V3 achieved a mean execution time of 1058.3 ms for CPU and 634.5 ms for GPU. We conclude that the mobile application proposed can be successfully used to run mobile networks and traditional networks for image classification, but the balance between accuracy and execution time needs to be carefully assessed. This mobile app is a tool for researchers, policymakers, non-governmental organizations, and the general public who intends to assess the tree species, providing a GUI-based platform for non-programmers to access the capabilities of deep learning models in complex classification tasks. • Deep learning models executed in mobile applications can classify tree species in RGB. • Traditional networks are better (F1-score of 97.4%) than mobile (83.8%) networks. • Mobile networks are faster than traditional models in the mean execution time on task.

State of Art IoT and Edge Embedded Systems for Real-Time Machine Vision Applications

IoT and edge devices dedicated to run machine vision algorithms are usually few years lagging currently available state-of-the-art technologies for hardware accelerators. This is mainly due to the non-negligible time delay required to implement and assess related algorithms. Among possible hardware platforms which are potentially being explored to handle real-time machine vision tasks, multi-core CPU and Graphical Processing Unit (GPU) platforms remain the most widely used ones over Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC)-based platforms. This is mainly due to the availability of powerful and user friendly software development tools, in addition to their lower cost, and obviously their high computation power with reasonable form factor and power consumption. Nevertheless, the trend now is towards a System-On-Chip (SOC) processors which combine ASIC/FPGA accelerators with GPU/multicore CPUs. This paper presents different state of the art IoT and edge machine vision technologies along with their performance and limitations. It can be a good reference for researchers involved in designing state of the art IoT embedded systems for machine vision applications.

Versatile and Concurrent FPGA-Based Architecture for Practical Quantum Communication Systems

This article presents a hardware and software architecture, which can be used in those systems that implement practical quantum key distribution (QKD) and quantum random-number generation (QRNG) schemes. This architecture fully exploits the capability of a System on a Chip (SoC), which comprehends both a field-programmable gate array (FPGA) and a dual-core CPU unit. By assigning the time-related tasks to the FPGA and the management to the CPU, we built a flexible system with optimized resource sharing on a commercial off-the-shelf (COTS) evaluation board, which includes an SoC. Furthermore, by changing the dataflow direction, the versatile system architecture can be exploited as a QKD transmitter, QKD receiver, and QRNG control-acquiring unit. Finally, we exploited the dual-core functionality and realized a concurrent stream device to implement a practical QKD transmitter, where one core continuously receives fresh data at a sustained rate from an external QRNG source, while the other operates with the FPGA to drive the qubit transmission to the QKD receiver. The system was successfully tested on a long-term run proving its stability and security. This demonstration paves the way toward a more secure QKD implementation, with fully unconditional security as the QKD states are entirely generated by a true random process and not by deterministic expansion algorithms. Eventually, this enables the realization of a standalone quantum transmitter, including both the random numbers and the qubit generation.

A User-Centric Data Protection Method for Cloud Storage Based on Invertible DWT

Protection on end users’ data stored in Cloud servers becomes an important issue in today’s Cloud environments. In this paper, we present a novel data protection method combining Selective Encryption (SE) concept with fragmentation and dispersion on storage. Our method is based on the invertible Discrete Wavelet Transform (DWT) to divide agnostic data into three fragments with three different levels of protection. Then, these three fragments can be dispersed over different storage areas with different levels of trustworthiness to protect end users’ data by resisting possible leaks in Clouds. Thus, our method optimizes the storage cost by saving expensive, private, and secure storage spaces and utilizing cheap but low trustworthy storage space. We have intensive security analysis performed to verify the high protection level of our method. Additionally, the efficiency is proved by implementation of deploying tasks between CPU and General Purpose Graphic Processing Unit (GPGPU) in an optimized manner.

Joint scene and object tracking for cost-Effective augmented reality guided patient positioning in radiation therapy

Background and ObjectiveThe research is done in the field of Augmented Reality (AR) for patient positioning in radiation therapy is scarce. We propose an efficient and cost-effective algorithm for tracking the scene and the patient to interactively assist the patient’s positioning process by providing visual feedback to the operator. Up to our knowledge, this is the first framework that can be employed for mobile interactive AR to guide patient positioning. MethodsWe propose a pointcloud processing method that, combined with a fiducial marker-mapper algorithm and the generalized ICP algorithm, tracks the patient and the camera precisely and efficiently only using the CPU unit. The 3D reference model and body marker map alignment is calculated employing an efficient body reconstruction algorithm. ResultsOur quantitative evaluation shows that the proposed method achieves a translational and rotational error of 4.17 mm/0.82∘ at 9 fps. Furthermore, the qualitative results demonstrate the usefulness of our algorithm in patient positioning on different human subjects. ConclusionSince our algorithm achieves a relatively high frame rate and accuracy employing a regular laptop (without a dedicated GPU), it is a very cost-effective AR-based patient positioning method. It also opens the way for other researchers by introducing a framework that could be improved upon for better mobile interactive AR patient positioning solutions in the future.

A TENSORFLOW BASED METHOD FOR LOCAL DERIVATIVE PATTERN

Python programming language provides a very convenient environment of implementing machine learning applications. However, programmers usually faced with a poor performance compared to compiled functions when they write script based programs that demands intense computations. TensorFlow framework provides acceleration by enabling the utilization of various computing resources such as multicore CPU and GPU unit as well as including various compiled algorithms for developing machine learning applications. In this way, algorithms developed using existing TensorFlow operations can shorten computation times by using these resources indirectly without requiring parallel programming or GPU programming. In this study, Local Derivative Pattern (LDP) analysis which is one of the efficient feature extraction approaches for machine learning models was realized using a TensorFlow based algorithm. Independent pixel based operations in LDP algorithm which requires intense computations, enable developing an efficient tensor based algorithm. The performance of the TensorFlow based algorithm has been measured by comparing it with the Python script version of the same algorithm. The results obtained for various sizes and numbers of sample images show that TensorFlow operations provide significant acceleration rates for the LDP algorithm.

Performance Analysis of Accelerator Architectures and Programming Models for Parareal Algorithm Solutions of Ordinary Differential Equations

Increasing needs for the study of complex dynamical systems require computing solutions of a large number of ordinary and partial differential time-dependent equations in near real-time. Numerical integration algorithms, which are computationally expensive and inherently sequential, are typically used to compute solutions of ordinary and partial differential time-dependent equations. This presents challenges to study complex dynamical systems in near real-time. This paper examines the challenges of computing solutions of ordinary differential time-dependent equations using the Parareal algorithm belonging to the class of parallel-in-time algorithms on various high-performance computing accelerator-based architectures and associated programming models. The paper presents the code refactoring steps and performance analysis of the Parareal algorithm on two accelerator computing architectures: the Intel Xeon Phi CPU and Graphics Processing Unit many-core architectures, and with OpenMP, OpenACC, and CUDA programming models. The speedup and scaling performance analysis are used to demonstrate the suitability of the Parareal to compute the solutions of a single ordinary differential time-dependent equation and a family of interdependent ordinary differential time-dependent. The speedup, weak and strong scaling results demonstrate the suitability of Graphical Processing Units with the CUDA programming model as the most efficient accelerator for computing solutions of ordinary differential time-dependent equations using parallel-in-time algorithms. Considering the time and effort required to refactor the code for execution on the accelerator architectures, the Graphical Processing Units with the OpenACC programming model is the most efficient accelerator for computing solutions of ordinary differential time-dependent equations using parallel-in-time algorithms.

ETM: Effective Tuning Method Based on Multi-Objective and Knowledge Transfer in Image Recognition

With the widespread application of machine learning and deep learning, image recognition has been continuously developed. However, there are still huge challenges in the use of machine learning and deep learning. The tuning processes of algorithms are critical and challenging for their performance. Although there have been many previous works to improve the final accuracy of the recognition algorithms through tuning, these works cannot consider some indicators that are also very important in the actual environment (such as latency, central processing unit (cpu) utilization) in the tuning. In this paper, we propose an effective tuning method based on multi-objective and knowledge transfer, which is solved the above limitations in the image recognition. Specifically, we first use an agent to automatically tune the recognition algorithms, and combine the prediction accuracy and the running latency of each episode as a multi-objective reward signal to guide the update of the internal parameters of the agent. In this way, the agent can continuously select the better algorithm configuration to improve prediction performance. In addition, we improve the efficiency of the above tuning process by transferring knowledge. To do that, we can learn the meta parameters from other small-scale tasks to initialize the agent. In the experiments, we apply the proposed method to tune the eXtreme Gradient Boosting and random forest on 57 image recognition tasks and convolutional neural network on 2 tasks. The experimental results verify that the proposed method achieves average accuracy rankings of 1.92, 1.42 and 1.71 on three algorithms to be optimized, respectively. Especially in terms of latency performance, the proposed method performs best on all the tasks (57 data sets) on the three algorithms to be optimized. In addition, we verify the various components of the proposed method through ablation experiments.

Detection of Binary Square Fiducial Markers Using an Event Camera

Event cameras are a new type of image sensors that output changes in light intensity (events) instead of absolute intensity values. They have a very high temporal resolution and a high dynamic range. In this paper, we propose a method to detect and decode binary square markers using an event camera. We detect the edges of the markers by detecting line segments in an image created from events in the current packet. The line segments are combined to form marker candidates. The bit value of marker cells is decoded using the events on their borders. To the best of our knowledge, no other approach exists for detecting square binary markers directly from an event camera using only the CPU unit in real-time. Experimental results show that the performance of our proposal is much superior to the one from the RGB ArUco marker detector. The proposed method can achieve the real-time performance on a single CPU thread.

Deep Learning based Optical Inspection with Centralized Analysis for High Volume Smart Manufacturing

Increased capabilities in data storage and exploration provide significant insights for quality assurance in a high volume manufacturing environment. However, these opportunities are associated with great challenges in analytical model development, application deployment, system throughput and reliability. While no commercial software system fully meets the needs of recording head factories in Seagate, a novel strategy named optical inspection with centralized analysis has been developed to detect defects of trailing edges of the recording heads of hard disk drives, and fail the parts when necessary. Leveraging the state-of-the-art artificial intelligence technologies, a deep learning based semantic segmentation engine is built using convolutional neural networks for optical inspection. It has shown an improved accuracy to that of visual inspection performed by human. Meanwhile, a high performance computation engine has been built as a Kubernetes cluster with multiple GPU and CPU units. It is able to achieve the target throughput of three million high-resolution images in each day (i.e., 12 TB image data and 35 images per second). With the high fidelity offered by Kubernetes cluster, the developed applications (inference engine, preprocessor, postprocessor, etc.) serve as containerized microservices independently. Such an architecture ensures the vertical and horizontal scalabilities according to the computation of each individual deployment, while all deployments communicate through an Advanced Message Queuing Protocol cluster without human interference. This analytic framework enables Industry 4.0 recording head manufacturing by integrating advanced AI technologies with a robust edge computation architecture.

Data Mining dalam Akurasi Tingkat Kelayakan Pakai terhadap Peralatan Perangkat Keras

Hardware is a computer that can be seen and touched in person. Hardware is used to support student work and learning processes. The hardware should always be in good shape. If any damage should be done quickly. The benefits of this study provide a viable level of data against hardware tools. The purpose of this study determines that hardware that is worth using quickly and precisely so easily can be repaired and replaced. Hard-processed action consists of 12 projectors, 2 units of access point, 6 units of monitors, and 20 CPU units. To see the level of appropriateness regarding hard drives requires a rough set algorithm with that stage: information system; Decision system; Equivalency class; Discernibility matrix; Discernibility Matrix module D; Reduction; Generate Rules. The results of the 40 devices of study STMIK Indonesia Padang subtract college have 10 rules of policy on whether the hardware is still viable, repaired or replaced. So using a rough set algorithm is particularly appropriate to apply in a verifiable level of accuracy to fast and precise hardware.

Event-Based Implementation of Fractional Order IMC Controllers for Simple FOPDT Processes

Fractional order calculus has been used to generalize various types of controllers, including internal model controllers (IMC). The focus of this manuscript is towards fractional order IMCs for first order plus dead-time (FOPDT) processes, including delay and lag dominant ones. The design is novel at it is based on a new approximation approach, the non-rational transfer function method. This allows for a more accurate approximation of the process dead-time and ensures an improved closed loop response. The main problem with fractional order controllers is concerned with their implementation as higher order transfer functions. In cases where central processing unit CPU, bandwidth allocation, and energy usage are limited, resources need to be efficiently managed. This can be achieved using an event-based implementation. The novelty of this paper resides in such an event-based algorithm for fractional order IMC (FO-IMC) controllers. Numerical results are provided for lag and delay dominant FOPDT processes. For comparison purposes, an integer order PI controller, tuned according to the same performance specifications as the FO-IMC, is also implemented as an event-based control strategy. The numerical results show that the proposed event-based implementation for the FO-IMC controller is suitable and provides for a smaller computational effort, thus being more suitable in various industrial applications.

An Effective SAT Solver Utilizing ACO Based on Heterogenous Systems

This paper presents new parallel strategies for preprocessing and solving the issue of Boolean Satisfaction (SAT) on Heterogeneous systems of multicore and many-core CPU and Graphics Processing Unit (GPU) using Open Multi-Processor (OpenMP) and NVIDIA - CUDA. We propose exceptionally proficient and parallel techniques for SAT simplifications using the variable elimination method based on the Davis-Putnam-Logemann-Loveland (DPLL) slitting rule algorithm performed with a shared-memory model on a multicore CPU platform, where the clause elimination subsumption and the pure-literal removal techniques are completely performed on the CUDA framework. We demonstrate how efficient an evolutionary SAT solver is by using the suggested heterogeneous pre-processing, leading to important acceleration improvements in the solution's quality enhancement. The penalization of the transformative SAT solver is executed with Ant Colony Optimization (ACO) scheme utilizing CUDA. (Compute Unified Device Architecture) We perform thorough benchmarks to test the performance of our preprocessor and solver implementations against various random SAT formulas. The promoted H-SAT pre-processor scheme has gotten a speed-up of a factor 15x over the sequential implementation with statistical reductions on the original CNF which becomes up to 49% and 43% in case of literals and clauses numbers exclusively, where the H-SAT gain strength the solvability of the ACO solver by 100% in some cases.

Heterogeneous data-processing optimization with CLARA’s adaptive workflow orchestrator

The hardware landscape used in HEP and NP is changing from homogeneous multi-core systems towards heterogeneous systems with many different computing units, each with their own characteristics. To achieve maximum performance with data processing, the main challenge is to place the right computing on the right hardware. In this paper, we discuss CLAS12 charge particle tracking workflow orchestration that allows us to utilize both CPU and GPU to improve the performance. The tracking application algorithm was decomposed into micro-services that are deployed on CPU and GPU processing units, where the best features of both are intelligently combined to achieve maximum performance. In this heterogeneous environment, CLARA aims to match the requirements of each micro-service to the strength of a CPU or a GPU architecture. A predefined execution of a micro-service on a CPU or a GPU may not be the most optimal solution due to the streaming data-quantum size and the data-quantum transfer latency between CPU and GPU. So, the CLARA workflow orchestrator is designed to dynamically assign micro-service execution to a CPU or a GPU, based on the online benchmark results analyzed for a period of real-time data-processing.

Mass and Energy Densities of the Cosmos

Fundamental principles of classical (Newtonian) physics are employed to probe the cosmological lambda Λ; it yields the values ρvac = 2.61 x 10-39 g cm-3 and Λ = 4.87 x 10-66 cm-2. It is revealed that Λ is a fundamental physical constant defined by vacuum density-light speed ρvacc2 correlation. However, the constant accelerates along the groups and periods of a universal spatial periodicity equivalent to the chemical periodicity. Previous results are cited to show that chemical elements are quantum harmonic (periodic) oscillators QHOs and their waveform oscillations exclusively define the vacuum field. The cosmological periodic unit CPU is introduced, it relies on the cosmological principle to argue that a relative physical quantity evaluated for the QHO applies to constituents of corresponding cosmological spatial quanta. Compelling evidences, backed with relevant data and quantitative expressions, are presented to argue that: there was never a big bang, it is a Linde-universe sans “chaotic”; nature posts no singularity; mass does not curve spacetime, neither does metric space curvature trace directly to gravitation nor particle creation, gravity is classical, not quantum; reality is quadri-phasic not mono-phasic with a clear distinction between the atomic waveform defined with absolute atomic mass and condensed matter defined with relative atomic mass; every chemical element exists in three particle-generations thus, dark matter is invisible form (conjugate) of the visible element, its waveform manifests dark energy, it is not implicated in metric space expansion; Planck scale does not exist, radioactivity constrains fundamental length to atomic radius of the heaviest element.

SIFT-aided path-independent digital image correlation accelerated by parallel computing

Current iterative digital image correlation (DIC) algorithms can efficiently converge at the deformation vector with high accuracy when they are fed with reliable initial guess. Thus, the adaptability of DIC method is dominated to a large extent by the estimation of initial guess. In recent years, image feature-based technique, especially the scale-invariant feature transform (SIFT), was introduced to DIC for the estimation of initial guess in the case of large and complex deformation, due to its robustness in handling the images with translation, rotation, scaling, and localized distortion. However, feature extraction and matching in SIFT are very time consuming, which limits the applications of the SIFT-aided DIC. In this study, we developed a SIFT-aided path-independent DIC method and accelerated it by introducing the parallel computing on graphics processing unit (GPU) or multi-core CPU. In our method, SIFT features are used to estimate the initial guess for the inverse compositional Gauss-Newton (IC-GN) algorithm at each point of interest (POI). The experimental study shows that the developed method can deal with large and inhomogeneous deformation with high accuracy. Parallel computing (especially on GPU) accelerates significantly the proposed DIC method. The achieved computation speed satisfies the need for real-time processing with high resolution for the images of normal sizes.