Evaluating Embedded FPGA Accelerators for Deep Learning Applications

Abstract

FPGA-based embedded soft vector processors can exceed the performance and energy-efficiency of embedded GPUs and DSPs for lightweight deep learning applications. For low complexity deep neural networks targeting resource constrained platforms, we develop optimized Caffe-compatible deep learning library routines that target a range of embedded accelerator-based systems between 4 -- 8 W power budgets such as the Xilinx Zedboard (with MXP soft vector processor), NVIDIA Jetson TK1 (GPU), InForce 6410 (DSP), TI EVM5432 (DSP) as well as the Adapteva Parallella board (custom multi-core with NoC). For MNIST (28×28 images) and CIFAR10 (32×32 images), the deep layer structure is amenable to MXP-enhanced FPGA mappings to deliver 1.4 -- 5× higher energy efficiency than all other platforms. Not surprisingly, embedded GPU works better for complex networks with large image resolutions.