  Transformer is a deep learning language model widely used for natural
language processing (NLP) services in datacenters. Among transformer models,
Generative Pre-trained Transformer (GPT) has achieved remarkable performance in
text generation, or natural language generation (NLG), which needs the
processing of a large input context in the summarization stage, followed by the
generation stage that produces a single word at a time. The conventional
platforms such as GPU are specialized for the parallel processing of large
inputs in the summarization stage, but their performance significantly degrades
in the generation stage due to its sequential characteristic. Therefore, an
efficient hardware platform is required to address the high latency caused by
the sequential characteristic of text generation.
  In this paper, we present DFX, a multi-FPGA acceleration appliance that
executes GPT-2 model inference end-to-end with low latency and high throughput
in both summarization and generation stages. DFX uses model parallelism and
optimized dataflow that is model-and-hardware-aware for fast simultaneous
workload execution among devices. Its compute cores operate on custom
instructions and provide GPT-2 operations end-to-end. We implement the proposed
hardware architecture on four Xilinx Alveo U280 FPGAs and utilize all of the
channels of the high bandwidth memory (HBM) and the maximum number of compute
resources for high hardware efficiency. DFX achieves 5.58x speedup and 3.99x
energy efficiency over four NVIDIA V100 GPUs on the modern GPT-2 model. DFX is
also 8.21x more cost-effective than the GPU appliance, suggesting that it is a
promising solution for text generation workloads in cloud datacenters.
