Intel CPU已经自带集成显卡，软件通过Intel MSDK能够开启硬件的编解码加速功能。下表根据市场上的主流CPU（i3/i5/i7）绘制了一张编解码的实际硬件能力参考图，便于CODEC开发人员参考。
Optimizing graphics for Intel® Core™ processors as well as Intel® Atom™ processors is rapidly becoming a strategic imperative for game developers. This article describes how Funcom developed LEGO* Minifigures Online (LMO)to provide exceptional graphical experiences and improved battery life on both platforms.
Project Newton includes the connection of all main platforms (Intel® Core™ processor, Intel® Centrino* processor technology, Intel® Atom™ processor, ARM* mobile platforms) and all IoT platforms (Intel® Edison board, Intel® Galileo board, Raspberry*, Spark*, Mbed*, Freescale*, Arduino Uno*, etc.). Thus, Project Newton can connect platforms running all current mainstream OSs (Windows*, Linux*,...
Wolfgang Engel, CEO of Confetti, describes how to pick different resource binding mechanisms to run an application efficiently on specific Intel’s GPUs, folowing the release of Windows* 10 and the 6th generation Intel® Core™ processor family (code-name Skylake). Microsoft DirectX* 12
The Intel® PCLMULQDQ instruction is a new instruction available beginning with the Intel® Core™ processor family. The PCLMULQDQ instruction performs a carry-less multiplication of two 64-bit operands.
The fundamental shift in processor performance from clock speed to multi-cpu means game designs must evolve to effectively utilize the available processor cycles. This article discusses key features of the Intel® Core™ i7 processor for game development.
As Moore’s Law drives the silicon industry towards higher transistor counts, processor designs are becoming more and more complex. The area of development includes core count, execution ports, vector units, uncore architecture and finally instruction sets. This increasing complexity leads us to a place where access to the shared memory is the major limiting factor, resulting in feeding the cores...
Explains which Intel® Compiler switches to use to target and optimize for a specific platform, microarchitecture, CPU or processor.
Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and one of the most popular community frameworks for image recognition. Caffe is often used as a benchmark together with AlexNet*, a neural network topology for image recognition, and ImageNet*, a database of labeled images.