Intel I7 Research Articles

Identification of Cactus Species Using the MobileNet Convolutional Neural Network Model

The objective of this design is to create a system capable of accurately and precisely classifying different types of succulent cacti. The system design aims to create a succulent cactus classification application using a Convolutional Neural Network (CNN) with the MobileNet architecture. The process involves collecting cactus images, dividing them into training and test datasets, and developing a CNN model to recognize patterns in succulent cactus species. The training data is used to train the CNN model, and the test data evaluates the model's accuracy. The trained model, stored in Tf.lite format, successfully classifies 15 cactus types, achieving high accuracy by employing preprocessing steps like resizing, normalization, and background removal. Over 1,200 cactus images were taken with a smartphone, categorized into 15 classes, and prepared to ensure optimal lighting, angle, background, and resolution (224x224 pixels). The MobileNet model was chosen for its high accuracy and efficiency. Hardware used includes a Samsung A54 smartphone and an Intel i7 laptop, with software such as Python, Kotlin, and Android Studio facilitating development. This design ensures the application’s accessibility, making it a valuable tool for cactus enthusiasts and the general public to easily identify different succulent cactus types. Testing of the cactus species classification program using the Convolutional Neural Network (CNN) method with the MobileNetV2 architecture yielded strong results, achieving over 90% accuracy in classifying 15 cactus species. The highest training accuracy of 0.9837 was achieved at 150 epochs without early stopping, outperforming other epoch configurations. The model successfully classified species across five main genera—Kalanchoe, Crassula, Echeveria, Haworthia, and Euphorbia. This high accuracy highlights the model's effectiveness, making it a useful tool for cactus enthusiasts and the public to accurately identify and distinguish cactus species.

Performance Comparison of Selected Filters in Fast Denoising of Oil Palm Hyperspectral Data

Usually, hyperspectral data captured from an airborne UAV or satellite contain some noise that can be severe in some channels. Often, channels that are badly affected by the noise are discarded. This is because the corrupted channels cannot be reclaimed by common filtering techniques, making important information in the affected channels different from those of field spectroscopy of similar wavelengths. In this study, a fast-denoising method is implemented on some channels of oil palm hyperspectral data that are badly affected by noise. The amount of noise is unknown, and it varies across the noisy channels from bad to severe. This is different from the data normally used by many studies, which are essentially clean data spiked with mild noise of known variance. The process starts by identifying which noisy channels to filter based on the level of the estimated noise in them. Then, filtering is conducted within each channel and across channels. Once the noise is removed, the improvement in signal-to-noise ratio (SNR) is calculated for each channel. The performance of Kalman, Wiener, Savitzky–Golay, wavelet, and cosine filters is tested in the same framework and the results are compared in terms of execution time, signal-to-noise ratio, and visual quality. The results show that the Kalman filter slightly outperformed the other filters. The proposed scheme was implemented using MATLAB R2023b running on an Intel i7 processor, and the average execution time was less than 1 second for each channel. To the best of our knowledge, this is the first attempt to filter real oil palm hyperspectral data containing speckle noise using a Kalman filter. This technique can be a useful tool to those working in the oil palm industry.

Sorting Algorithms Comparison on FPGA and Intel i7 Architectures

Sorting Algorithms Comparison on FPGA and Intel i7 Architectures

A hybrid PCA acceleration method for rapid real-time 2D MRI.

Objective.To develop a 2D MR acceleration method utilizing principal component analysis (PCA) in a hybrid fashion for rapid real-time applications.Approach.Retrospective testing was performed on 10 lung, 10 liver and 10 prostate 3T MRI data sets for image quality and target contourability. Sampling of k-space is performed by acquiring central (low-frequency) data in every frame while the high-frequency data is incoherently undersampled such that all of k-space is acquired in a pre-determined number of frames. Firstly, principal components (PCs) representative of intra-frame correlations between central and outer k-space data are used to estimate unsampled data in the frame of interest. Then to add further stability, PCs representative of time-domain fluctuations within a reconstruction window of the most recent frames are fit to outer k-space data (including above estimations) to obtain final estimates in the frame of interest. Accelerated reconstructions between 3x and 8x were tested for image quality and contourability along with the optimal number of PCs for fitting.Main results.It was found that at higher acceleration rates, image quality did not deteriorate significantly. Similarly, it was found that the images were of sufficient quality to contour a target using auto-contouring software at all tested acceleration rates and sites. SSIM values were found to be ⩾0.91 at all accelerations tested. Similarly dice coefficients at the different sites were found to be ⩾0.89 even at 8x accelerations which is on par with or better than intra-observer variation.Significance.This method appears to produce improved image quality and contourability compared to previous PCA methods while also allowing a greater number of PCs to be used in reconstruction. The method can be run using a simple single-channel coil and does not require significant computing power to meet real-time interventional standards (reconstruction times ∼60 ms/frame on Intel i5 CPU).

Open-Source Optimization of Hybrid Monte Carlo Methods for Fast Response Modeling of NaI (Tl) and HPGe Gamma Detectors

Modeling the response of gamma detectors has long been a challenge within the nuclear community. Significant research has been conducted to digitally replicate instruments that can cost over USD 100,000 and are difficult to operate outside of a laboratory setting. The large cost and availability prevent some from making use of such equipment. Subsequently, there have been multiple attempts to create cost-effective codes that replicate the response of sodium-iodide and high-purity germanium detectors for data derivation related to gamma-ray interaction with matter. While robust programs do exist, they are often subject to export controls and/or they are not intuitive to use. Through the use of hybrid Monte Carlo methods, MATLAB can be used to produce a fast first-order response of various gamma-ray detectors. The combination of a graphical user interface with a numerical-based script allows for open-source and intuitive code. When benchmarked with experimental data from Co-60, Cs-137, and Na-22, the code can numerically calculate a response comparable to experimental and industry-standard response codes. Evidence supports both savings in computational requirements and the inclusion of an intuitive user experience that does not heavily compromise data when compared to other standard codes, such as MCNP and GADRAS, or experimental results. When the application is installed on a Dell Intel i7 computer with 16 cores, the average time to simulate the benchmarked isotopes is 0.26 s. Installation on an HP Intel i7 four-core machine runs the same isotopes in 1.63 s. The results indicate that simple gamma detectors can be modeled in an open-source format. The anticipation for the MATLAB application is to be a tool that can be easily accessible and provide datasets for use in an academic setting requiring gamma-ray detectors. Ultimately, this article provides evidence that hybrid Monte Carlo codes in an open-source format can benefit the nuclear community in both computational time and up-front cost for access.

Exploring the trade-off between computational power and energy efficiency: An analysis of the evolution of quantum computing and its relation to classical computing

Quantum computing is considered a revolutionary technology due to its ability to solve computational problems that are beyond the capabilities of classical computers. However, quantum computing requires great amounts of energy to run. Therefore, a factor in deciding whether to use quantum computing should be not only the complexity of the problem to be solved, but also the energy required to solve it. This paper presents an empirical study developed with the aim of comparing classical and quantum computing in terms of energy efficiency to determine whether the increased power of quantum computers is offset by their higher energy consumption. To achieve this, a variety of problems with different levels of complexity were tested on both types of computers. Specifically, we used the IBM Quantum computers with a maximum of 5 qubits and an Intel i7, as a classical computer. In addition to this we have also analysed the evolution of the quantum computers, performing measurements on three time periods. Our empirical study showed that there is a variability of results obtained in the three time periods and that quantum computing is not recommended for low-complexity problems, given its high energy consumption, particularly when compared to traditional computing.

Physical mechanism-corrected degradation trend prediction network under data missing

Accurate degradation trend prediction (DTP) is crucial for optimizing equipment operation and maintenance, thereby boosting production efficiency. This study introduces a novel Data Repair and Dual-data-stream LSTM (DR-DLSTM) network to tackle the challenge of missing data in equipment DTP. The proposed DR-DLSTM framework employs convex optimization to consider both the trend and periodic variations in the data, incorporating polynomial and trigonometric functions into the implicit feature matrix to construct latent vectors for missing data rectification. The network features a Dual-LSTM block with dual data streams to enhance feature extraction, with two gating update units correlating time series components and redistributing feature weights. The Dual-LSTM enables separate and accurate prediction of trend and periodic components, thereby enhancing the feature extraction capability of the prediction model. Additionally, the integration of physical rule information through Fourier and wavelet transform frequency correction modules allows for dynamic adjustments in prediction outcomes, from global trends to localized details. The DR-DLSTM's effectiveness is demonstrated through comprehensive comparisons with state-of-the-art models across multiple datasets, highlighting its superior performance. The results demonstrate the superiority of the proposed model. These algorithms were implemented in Python using Torch on a 2.9 GHz Intel I7 CPU and TITAN Xp GPU.

BestCRM: An Exhaustive Search for Optimal Cis-Regulatory Modules in Promoters Accelerated by the Multidimensional Hash Function.

The concept of cis-regulatory modules located in gene promoters represents today's vision of the organization of gene transcriptional regulation. Such modules are a combination of two or more single, short DNA motifs. The bioinformatic identification of such modules belongs to so-called NP-hard problems with extreme computational complexity, and therefore, simplifications, assumptions, and heuristics are usually deployed to tackle the problem. In practice, this requires, first, many parameters to be set before the search, and second, it leads to the identification of locally optimal results. Here, a novel method is presented, aimed at identifying the cis-regulatory elements in gene promoters based on an exhaustive search of all the feasible modules' configurations. All required parameters are automatically estimated using positive and negative datasets. To be computationally efficient, the search is accelerated using a multidimensional hash function, allowing the search to complete in a few hours on a regular laptop (for example, a CPU Intel i7, 3.2 GH, 32 Gb RAM). Tests on an established benchmark and real data show better performance of BestCRM compared to the available methods according to several metrics like specificity, sensitivity, AUC, etc. A great practical advantage of the method is its minimum number of input parameters-apart from positive and negative promoters, only a desired level of module presence in promoters is required.

DESIGN AND IMPLEMENTATION OF REAL-TIME NON-LINEAR DYNAMIC SIMULATION OF FIGHTER AIRCRAFT ON MULTICORE PROCESSOR

Simulation is a system to imitate the operation of various kinds of real-world facilities or processes for training, behavior study or entertainment purposes. Some implementation of fighter aircraft simulation is done sequentially using FORTRAN and MATLAB / Simulink. On the other hand, a multicore (parallel) computer system has been in the personal computer environment, so the multicore computing paradigm should be considered. Intel Threading Building Blocks (TBB) library need to be utilized in multicore programming. This paper discusses the design and implementation of real-time nonlinear dynamic simulation of fighter aircraft on multicore processor. The aircraft model used in this study is F16 data. Implementation of the simulation code is written in C++ with Intel TBB, OpenGL, Open Scene Graph, and Open Dynamic Engine library. The testing results of non-linear dynamics simulation of fighter aircraft show that the speedup on an Intel Core 2 Duo processor is 1.1776 x for elevator doublet input (short period), while speedup on an Intel i7 is 1.5405 x for rudder doublet input (dutch roll). Key Words: multicore computing, real-time simulation, nonlinear dynamic, fighter aircraft model

Clinical validity and precision of deep learning-based cone-beam computed tomography automatic landmarking algorithm.

This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which were determined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The time required to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually, compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculate these measurements, the efficiency of diagnosis and treatment may be improved.

DLSC: Distributed Multi-Agent Trajectory Planning in Maze-Like Dynamic Environments Using Linear Safe Corridor

This article presents an online distributed trajectory planning algorithm for a quadrotor swarm in a maze-like dynamic environment. We utilize a dynamic linear safe corridor to construct the feasible collision constraints that can ensure interagent collision avoidance and consider the uncertainty of moving obstacles. We introduce mode-based subgoal planning to resolve deadlock faster in a complex environment using only previously shared information. For dynamic obstacle avoidance, we adopt heuristic methods such as collision alert propagation and escape point planning to deal with the situation where dynamic obstacles approach the agents clustered in a narrow corridor. We prove that the proposed algorithm guarantees the feasibility of the optimization problem for every replanning step. In an obstacle-free space, the proposed method can compute the trajectories for 60 agents on average 7.66 ms per agent with an Intel i7 laptop and shows the perfect success rate. Also, our method shows 64.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> shorter flight time than buffered Voronoi cell and 34.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> shorter than with our previous work. We conduct the simulation in a random forest and maze with four dynamic obstacles, and the proposed algorithm shows the highest success rate and shortest flight time compared to state-of-the-art baseline algorithms. In particular, the proposed algorithm shows over 97 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> success rate when the velocity of moving obstacles is below the agent's maximum speed. We validate the safety and robustness of the proposed algorithm through a hardware demonstration with ten quadrotors and two pedestrians in a maze-like environment.

ExoMDN: Rapid characterization of exoplanet interior structures with mixture density networks

Aims. Characterizing the interior structure of exoplanets is essential for understanding their diversity, formation, and evolution. As the interior of exoplanets is inaccessible to observations, an inverse problem must be solved, where numerical structure models need to conform to observable parameters such as mass and radius. This is a highly degenerate problem whose solution often relies on computationally expensive and time-consuming inference methods such as Markov chain Monte Carlo. Methods. We present ExoMDN, a machine-learning model for the interior characterization of exoplanets based on mixture density networks (MDN). The model is trained on a large dataset of more than 5.6 million synthetic planets below 25 Earth masses consisting of an iron core, a silicate mantle, a water and high-pressure ice layer, and a H/He atmosphere. We employ log-ratio transformations to convert the interior structure data into a form that the MDN can easily handle. Results. Given mass, radius, and equilibrium temperature, we show that ExoMDN can deliver a full posterior distribution of mass fractions and thicknesses of each planetary layer in under a second on a standard Intel i5 CPU. Observational uncertainties can be easily accounted for through repeated predictions from within the uncertainties. We used ExoMDN to characterize the interiors of 22 confirmed exoplanets with mass and radius uncertainties below 10 and 5%, respectively, including the well studied GJ 1214 b, GJ 486 b, and the TRAPPIST-1 planets. We discuss the inclusion of the fluid Love number k2 as an additional (potential) observable, showing how it can significantly reduce the degeneracy of interior structures. Utilizing the fast predictions of ExoMDN, we show that measuring k2 with an accuracy of 10% can constrain the thickness of core and mantle of an Earth analog to ≈13% of the true values.

“Whispering MLaaS”

While recent advancements of Deep Learning (DL) in solving complex real-world tasks have spurred their popularity, the usage of privacy-rich data for their training in varied applications has made them an overly-exposed threat surface for privacy violations. Moreover, the rapid adoption of cloud-based Machine-Learning-asa-Service (MLaaS) has broadened the threat surface to various remote side-channel attacks. In this paper, for the first time, we show one such privacy violation by observing a data-dependent timing side-channel (naming this to be Class-Leakage) originating from non-constant time branching operation in a widely popular DL framework, namely PyTorch. We further escalate this timing variability to a practical inference-time attack where an adversary with user level privileges and having hard-label black-box access to an MLaaS can exploit Class-Leakage to compromise the privacy of MLaaS users. DL models have also been shown to be vulnerable to Membership Inference Attack (MIA), where the primary objective of an adversary is to deduce whether any particular data has been used while training the model. Differential Privacy (DP) has been proposed in recent literature as a popular countermeasure against MIA, where inclusivity and exclusivity of a data-point in a dataset cannot be ascertained by definition. In this paper, we also demonstrate that the existence of a data-point within the training dataset of a DL model secured with DP can still be distinguished using the identified timing side-channel. In addition, we propose an efficient countermeasure to the problem by introducing constant-time branching operation that alleviates the Class-Leakage. We validate the approach using five pre-trained DL models trained on two standard benchmarking image classification datasets, CIFAR-10 and CIFAR-100, over two different computing environments having Intel Xeon and Intel i7 processors.

High-Performance Computation of the Number of Nested RNA Structures with 3D Parallel Tiled Code

Many current bioinformatics algorithms have been implemented in parallel programming code. Some of them have already reached the limits imposed by Amdahl’s law, but many can still be improved. In our paper, we present an approach allowing us to generate a high-performance code for calculating the number of RNA pairs. The approach allows us to generate parallel tiled code of the maximal dimension of tiles, which for the discussed algorithm is 3D. Experiments carried out by us on two modern multi-core computers, an Intel(R) Xeon(R) Gold 6326 (2.90 GHz, 2 physical units, 32 cores, 64 threads, 24 MB Cache) and Intel(R) i7(11700KF (3.6 GHz, 8 cores, 16 threads, 16 MB Cache), demonstrate a significant increase in performance and scalability of the generated parallel tiled code. For the Intel(R) Xeon(R) Gold 6326 and Intel(R) i7, target code speedup increases linearly with an increase in the number of threads. An approach presented in the paper to generate target code can be used by programmers to generate target parallel tiled code for other bioinformatics codes whose dependence patterns are similar to those of the code implementing the counting algorithm.

Cloud computing based deduplication using high-performance grade byte check and fuzzy search technique

Background: Data redundancy (DR) and data privacy (DP) is a critical issue that increases storage and security problems in cloud environments. Data de-duplication (DD) is one of the efficient backup storage techniques to reduce DR. The main problem with using cloud computing (CC) is more storage, the cost of deployment and maintenance. Objective: To minimize this problem, High-performance Grade Byte Check and Fuzzy search Techniques (HP-GBC-FST) based DD is proposed in this paper. Methods: The HP-GBC-FST is based on the pre-process of data by comparing their first byte and categorizing the byte based on the first byte. After DD, encryption has been processed on data to improve the data security in the cloud environment and then encrypted data is stored in the cloud. This HP-GBC-FST recognizes DR at the block level, reducing the redundancy of data more effectively. Then, HP-GBC-FST is created to detect and eliminate duplicates, improve security and storage efficiency (SE), reduce DD time and computation cost (CPC) in the DD verification and auditing phase. Result: The experiment has been conducted in an Intel I5 system and 500GB, 1Tb memory space and implemented in the Java programming environment. The results of the experiment reveal that the HP-GBC-FST improved the DD ratio and security by 3.7 and 97%, respectively, and reduced the DD time and CPC by 87% and 84.4%, respectively, over the existing technique. Conclusion: It concluded that the HP-GBC-FST has greater improvement over DD data in the cloud. Finally, the performance analysis of the HP-GBC-FST achieves higher storage, both privacy and security attributes, and incurs minimal CPC, DD time compared with the state he art research.

T-BFA: Targeted Bit-Flip Adversarial Weight Attack.

Traditional Deep Neural Network (DNN) security is mostly related to the well-known adversarial input example attack.Recently, another dimension of adversarial attack, namely, attack on DNN weight parameters, has been shown to be very powerful. Asa representative one, the Bit-Flip based adversarial weight Attack (BFA) injects an extremely small amount of faults into weight parameters to hijack the executing DNN function. Prior works of BFA focus on un-targeted attacks that can hack all inputs into a random output class by flipping a very small number of weight bits stored in computer memory. This paper proposes the first work oftargetedBFA based (T-BFA) adversarial weight attack on DNNs, which can intentionally mislead selected inputs to a target output class. The objective is achieved by identifying the weight bits that are highly associated with classification of a targeted output through a class-dependent weight bit searching algorithm. Our proposed T-BFA performance is successfully demonstrated on multiple DNN architectures for image classification tasks. For example, by merely flipping 27 out of 88 million weight bits of ResNet-18, our T-BFA can misclassify all the images from Hen class into Goose class (i.e., 100% attack success rate) in ImageNet dataset, while maintaining 59.35% validation accuracy.

Implementation of CAD/CAM program in a nonface-to-face classroom environment due to the COVID-19 pandemic.

This study aimed to evaluate a nonface-to-face crown designing module in a preclinical dental course. Free dental planning software (Blue Sky Plan) was installed on the personal computers of dental college students, and a #46 full veneer crown designing practice was performed individually. An online survey was conducted on the computers' specification and main usage of the students, the practice process, and results. Statistical analysis was conducted to analyze the association between variables, such as "operating system," "central processing unit ," "number of cores," "random-access memory (RAM)," "graphic card," and task performance. Of the D2 students, 75.4% (52 of 69) responded to the survey. Overall, 96% of the respondents used their computers, and all respondents had no problem running the program. Most of the students marked their level of computer literacy as intermediate and had purchased the computers for the purpose of performing light work. The most common specifications of the computer were Intel i5, quad core, 8 GB RAM, and Windows 10. Students had little experience with computer-aided design/computer-aided manufacturing before the class. The relationship between computer specifications and task performance was not statistically significant. Overall, students with intermediate-level computer literacy used computers with less than the recommended specifications of the program; however, they were able to run the program and individually proceed with modules to submit results. Using an individually available crown designing program can provide an opportunity to diversify curricula and broaden students' perspectives even under circumstances like the COVID-19 pandemic that limits intimate face-to face classes.

Standard versus uniform binary search and their variants in learned static indexing: The case of the searching on sorted data benchmarking software platform

AbstractLearned Indexes use a model to restrict the search of a sorted table to a smaller interval. Typically, a final binary search is done using the lower_bound routine of the Standard C++ library. Recent studies have shown that on current processors other search approaches (such as k‐ary search) can be more efficient in some applications. Using the SOSD learned indexing benchmarking software, we extend these results to show that k‐ary search is indeed a better choice when using learned indexes. We highlight how such a choice may be dependent on the computer architecture used, for example, Intel I7 or Apple M1, and provide guidelines for the selection of the Search routine within the learned indexing framework.

An enhanced binary classifier for Edge devices

Internet of Things, Edge Computing and 5G networks are rapidly becoming key enablers for a wide range of new services. This new infrastructure opens more possibilities for devices with embedded systems and microcontrollers to perform data processing and analysis. The need for real-time decisions now defeats the purpose of sending sensor data to the Cloud for further processing. Storing and analyzing data near its source introduce new constraints on resource-constrained and battery-powered devices. Scalable, Efficient, and Fast classifieR (SEFR) is one such algorithm that brings machine learning to low-power microcontrollers. The main contribution of this work is the definition of a new decision boundary, also known as the bias, to improve the accuracy of the SEFR binary classifier. Experiments performed on an 8-bit Arduino microcontroller using 5-fold cross-validation demonstrate that the improved algorithm (iSEFR) increases Precision, Recall and Accuracy by an average of 9%, 14% and 11% respectively. F1-score increases on average from 80% to 92%, which represents an increase of 12% in the model accuracy for even class distribution. MCC also increases from 64% to 85% by an average of 21%, and this represents a better binary classification quality. Training time and space complexity remain constant but the testing time increases marginally by an average of 0.0013 seconds for low-capacity processors. Moreover, iSEFR performs better on a 64-bit intel i5 processor with thousands of data points and features. Experiments also demonstrate that the accuracy of iSEFR is comparable to Support Vector Machine with a linear kernel.

Performance Evaluation of SHA-256 and BLAKE2b in Proof of Work Architecture

The popularity of blockchain, which is a technology with a distributed architecture, is increasing day by day due to the advantages it provides. Along with blockchain, interest in high-performance and efficient hash algorithm applications is increasing rapidly. While considering the amount of power and environmental problems required for these applications, more efficient algorithms were needed. In this study, a comparison of SHA-256 and BLAKE2b, one of the most popular algorithms, is presented. In the experiments, a standard Intel i5 based computing system is used. The benchmarking approach focuses on computationally heavy processes such as Proof of Work and Merkle Tree. This article presents a comparison of these two algorithms in a Bitcoin-like mining architecture.