Developer Kits with 9th Generation Intel® Core™ Processors
Develop and deploy solutions on Intel's latest high-performance platforms.
- Up to 46% better deep learning inference performance
- Preinstalled tools and SDKs included on kits
- Quick setup with preloaded samples
- Accelerate workloads on CPUs and GPUs
- Offload multiple workloads to optional Intel® Vision Accelerator products
Preinstalled Software Stack
Get the most out of your hardware performance with the Intel® Distribution of OpenVINO™ toolkit, Intel® Media SDK, Ubuntu* 18.04 desktop LTS, and libraries.
Pretrained Models for Acceleration
Choose from a variety of optimized detection and recognition models for developing deep learning applications.
Training Extensions for Deep Learning
Modify, customize, train, and extend computer vision models for deep learning and inference optimization.
Product and Performance Information
As measured by a ResNet-50 network with a batch size of 1 and int8 precision on Intel® Core™ i7-9700E vs. Intel® Core™ i7-7700 processor Intel® Core™ i7-9700E processor, PL1= 65 W TDP, 8C8T, Turbo up to 4.4 GHz, Intel® UHD Graphics 630, Motherboard: Asus* Prime Q370M-C, Memory: 2 times 8 GB DDR4-2666, Storage: 512 GB Intel® SSD 545s Series, OS: Ubuntu* 18.04 LTS (Bionic Beaver). Intel® Core™ i7-7700 processor, PL1=65W TDP, 4C8T, Turbo up to 4.2 GHz, Intel® HD Graphics 630, Motherboard: Asus* Prime Q270M-C, Memory: 2 times 8 GB DDR4-2400, Storage: 512 GB Intel® SSD 545s Series, OS: Ubuntu 18.04 LTS (Bionic Beaver). ResNet-50 is a popular CNN (Convoluted Neural Network) architecture that uses inception modules for computer vision recognition tasks (such as image classification). Image classification is the task of classifying a given image into one of the predefined categories. Reported metrics: Performance = Images/Second Performance results are based on testing as of May 24, 2019 and may not reflect all publicly available security updates. No product can be absolutely secure.