GPUs and DSPs offer very high-parallelism and impressive memory bandwidths, within the scope of a fully programmable platform. However, they need to fetch and decode every instruction, and must have a relatively fixed architecture, which leads to wasted energy. The Single-Instruction-Multiple-Data architecture of most high-performance GPUs also leads to reduced performance and reduced energy efficiency when threads can take different execution paths (the so called "divergence" problem).

FPGAs, on the other hand, provide a fully customizable architecture. For example, the precision of each computation can be tailored specifically for the application at hand. Moreover control is fully application specific and hardwired. Finally, their memory architecture can be specialized as much as needed, much beyond the DRAM/SRAM/register hierarchy that DSPs and GPUS provide.

Design costs stemming from low-level RTL design and the difficulty to reuse a significant portion of past designs have been a significant adoption hurdle for FPGAs in rapidly evolving application domains. This has recently changed, thanks to the advent of high-level synthesis, which allows a design team to quickly explore one or several highly optimized architectures from essentially the same software model, written e.g. in CUDA or OpenCl, that has been used to implement the same algorithm on a CPU or GPU. A very broad set of highly optimized low-level libraries written in these languages (e.g. CUBLAS, CUDNN, ...) is available to ease the task of accelerating machine learning, computer vision, image recognition, database search and other applications on FPGAs.

Who should attend:

• Programmers interested in learning how to efficiently implement highly
  parallel applications on FPGAs

What you will learn:

• Code optimization strategies to efficiently map OpenCl and C++ code
  on FPGAs
• Difference between memory architectures on GPUs and FPGAs
• How to select the best platform for the application at hand
• Migration path to ASICs when the algorithms are consolidated and
  manufacturing costs must be reduced