I7 8700

Manufactured by NVIDIA

The Intel Core i7-8700 is a 6-core, 12-thread desktop processor. It has a base clock speed of 3.2 GHz and a maximum turbo boost speed of 4.6 GHz. The i7-8700 is built on Intel's 14nm Coffee Lake architecture and features 12MB of cache.

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Lab products found in correlation

Gtx1060, by NVIDIA (1 mentions) Cuda toolkit 10, by NVIDIA (1 mentions) Geforce rtx 2070 super, by NVIDIA (1 mentions)

Close spelling variants of this product

I7-8700 CPU R) Core(TM) i7-8700 CPU Core i7-8700

4 protocols using i7 8700

AI Model Training and Evaluation

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Model training. Here is the information on experimental environment: Window10, Intel(R) Core(TM) i7-8700 CPU @3.20GHZ processor, RAM 16G, graphics card NVIDIA GTX1060, Python3.7. Model parameters are set as follows. Input image size is 416 × 416. The batch is set to 4, and the label smoothing is set to 0.05. The breakpoint continuation training method is adopted. One breakpoint is set every 350 times, four breakpoints are set, 350 weight files are generated after 350 times of training, and the best weight file is manually selected as the initial weight of the next breakpoint. The total number of training times is 1400 times. During the test, the confidence is set to 0.4, and IOU is set to 0.4.

Evaluation metrics. In this paper, we mainly evaluate the effectiveness of model training in terms of detection accuracy and efficiency. The evaluation metric used is the mean Average Precision (mAP), the average detection accuracy AP of all categories, and the number of image frames per second FPS detected by the algorithm.

Li H., Li B., Liu G., Wen X., Wang H., Wang X., Zhang S., Zhai Z, & Yang W. (2022). A detection and configuration method for welding completeness in the automotive body-in-white panel based on digital twin. Scientific Reports, 12, 7929.

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Optimized GPU-Accelerated Deep Learning

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The computations were performed on a Lenovo computer with a Windows 10 (64-bit) operating system, an Intel (R) Core (TM) I7-8700 @3.20 GHz CPU, an NVIDIA GeForce RTX2060 graphics card with 16.0 GB of RAM. For GPU acceleration, a computing platform (NVIDIA CUDA Toolkit 10.1) and a deep neural network acceleration library (NVIDIA cuDNN v7.6.5) were used. All models were implemented on TensorFlow 2.1.0 framework and deep learning library Keras 2.3.1 using Python 3.7.3 in Spyder IDE (v. 3.3.3).

Yu G., Ma B., Li H., Hu Y, & Li Y. (2022). Discrimination of Pesticide Residue Levels on the Hami Melon Surface Using Multiscale Convolution. Foods, 11(23), 3881.

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GPU-Accelerated Signal Processing and Model Retraining

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Work was performed on either a computer with an Intel(R) Core(TM) i7–8700 (clock speed: 3.20GHz), 32.0 GB RAM, and a NVIDIA GeForce RTX 2080 GPU or an Intel(R) Core(TM) i9–11900F (clock speed: 2.50GHz, 128 GB RAM, and a NVIDEA GeForce RTX 3090 GPU. Both ran Windows 10 Pro and MATLAB R2023b. Using a GPU with specifications similar to the ones listed here is highly recommended since it speeds up the computationally demanding processes such as the wavelet transform of signals and the network retraining by ~7 times faster than using CPU only.

Katsuki F., Spratt T.J., Brown R.E., Basheer R, & Uygun D.S. (2023). Sleep-Deep-Net learns sleep wake scoring from the end-user and completes each record in their style. bioRxiv.

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Leukocyte Recognition with CNN

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The hardware consisted of Intel(R) Core (TM) i7-8700 3.2GHz for CPU, NVIDIA GeForce RTX 2070 Super with 8GB memory for GPU (8.2 TFLOPS). Nnabla (SONY.INC) was used as Neural Network Libraries [6] , and Anaconda3.0 and Python3.5 were used as the development environment. In this study, Convolutional Neural Network (CNN) based object detection algorithm, ResNet-18 was used for AI model generation of leukocyte recognition. [7] . We performed transfer learning and fine tuning in 1000 epoch with each training dataset, and then we evaluated the accuracy, precision and F-measure of the generated AI models using clinical assessment images. Statistical analysis was carried out with paired t-test. P-value less than 0.05 was considered statistically significant.

Nozaka H., Oda M., Sasaki A., Harako H., Miyazaki M., Kaga S., Sakaiya N., Kudo K., Kimura S., Nakano M., Fujioka M, & Yamagata K. (2022). The Effect of Data Augmentation in Deep Learning Approach for Peripheral Blood Leukocyte Recognition. Studies in health technology and informatics, 290.

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