Artificial Intelligence Resource Center
In this paper, we explore FPGA miniﬂoat implementations (ﬂoating-point representations with non-standard exponent and mantissa sizes), and show the use of a block-ﬂoating point implementation that shares the exponent across many numbers, reducing the logic required to perform ﬂoating-point operations.
In this paper, we introduce a domain-specifc approach to overlays that leverages both software and hardware optimizations to achieve state-of-the-art performance on the FPGA for neural network acceleration.
This paper examines ﬂexibility, and its impact on FPGA design methodology, physical design tools and computer-aided design (CAD). We describe the degrees of ﬂexibility required to create efficient deep learning accelerators.
This white paper examines the future of deep neural networks, including sparse networks, low precision, and ultra-low precision, and compares the performance of Intel® Arria® 10 and Intel® Stratix® 10 FPGAs against NVIDIA graphics processing units (GPUs).
- Accelerating Deep Learning with the OpenCL™ Platform and Intel® Stratix® 10 FPGAs ›
This white paper describes how Intel® FPGAs leverage the OpenCLTM platform to meet the image processing and classification needs of today's image-centric world.
This white paper provides a detailed look at the architecture and performance of our Deep Learning Accelerator intellectual property (IP) core.
- Boost Performance of Video Analytics with the Intel® Vision Accelerator Design with Intel Arria® 10 FPGA ›
Build high-performance computer vision applications with integrated deep learning inference
The Intel® Vision Accelerator Design with Intel Arria 10 FPGA offers exceptional performance, flexibility, and scalability for deep learning and computer vision solutions.
- Intel® FPGAs Power Microsoft* Project Brainwave AI ›
- Microsoft Turbocharges AI with Intel FPGAs. You Can, Too ›
- Real-Time AI: Microsoft Announces Preview of Project Brainwave ›
- Intel FPGAs Bring Power to Artificial Intelligence in Microsoft Azure* ›
- Machine Learning on Intel FPGAs ›
- Microsoft’s Plans for FPGAs in Azure* Should Worry Traditional Chipmakers ›
- FPGAs for Deep Learning-Based Vision Processing ›
- Myth Busted: General Purpose CPUs Can’t Tackle Deep Neural Network Training ›
- CERN openlab Explores New CPU/FPGA Processing Solutions ›
- Making Sense of When to Use FPGAs ›
- Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Learning? ›
- FPGA-Based AI System Recognizes Faces at 1,000 Images per Second ›
- Accelerate Computer Vision and Deep Learning with OpenVINO™ Toolkit ›
- Intel® FPGAs Powering Real-Time AI Inferencing ›
- OpenVINO™ Toolkit and FPGAs: A look at the FPGA targeting of this versatile visual computing toolkit ›