Artificial Intelligence Resource Center

• Harnessing Numerical Flexibility for Deep Learning on FPGAs

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.

• Compiler and FPGA Overlay for Neural Network Inference Acceleration

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.

• Flexibility: FPGAs and CAD in Deep Learning 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 efcient deep learning accelerators.

• Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks?

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 OpenCL^TM platform to meet the image processing and classification needs of today's image-centric world.

• An OpenCL Deep Learning Accelerator on Intel® Arria® 10

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

• Intel® Vision Accelerator Design with Intel Arria® 10 FPGA – White Paper.
  
  The Intel® Vision Accelerator Design with Intel Arria® 10 FPGA offers exceptional performance, flexibility, and scalability for deep learning and computer vision solutions.