Intel FPGA Architecture Focuses on Deep Learning Inference

There has been much written about the potential for FPGAs to take a leadership role in accelerating deep learning but in practice, the hurdles of getting from concept to high performance hardware design are still taller than many AI shops are willing to scale, particularly when GPUs dominate in training and in a pinch, standard CPUs will do just fine for datacenter inference since they involve little developer overhead. Still, companies like Xilinx and competitor Intel with its Altera assets are working toward making deep learning on FPGAs easier with a variety of techniques that reproduce key elements of deep learning workflows in inference specifically since that is where the energy efficiency and performance story is clearest. For instance, the programmable solutions group at Intel where the Altera teams were integrated post-acquisition has just developed an FPGA overlay for deep learning inference that demonstrates some respectable results on an Arria 10 1150 FPGA. "Intel's DLA (deep learning accelerator) is a software-programmable hardware overlay on FPGAs to realize the ease of use of software programmability and the efficiency of custom hardware designs." The team explains that for the hardware side of DLA they have partitioned configurable parameters into the runtime to quickly use different neural network frameworks.