Scalable Low-Latency Persistent Neural Machine Translation on CPU Server with Multiple FPGAs

Abstract

We present a CPU server with multiple FPGAs that is purely software-programmable by a unified framework to enable flexible implementation of modern real-life complex AI that scales to large model size (100M+ parameters), while delivering real-time inference latency (~ms). Using multiple FPGAs, we scale by keeping a large model persistent in on-chip memories across FPGAs to avoid costly off-chip accesses. We study systems with 1 to 8 FPGAs for different devices: Intel® Arria® 10, Stratix® 10, and a research Stratix 10 with an AI chiplet. We present the first multi-FPGA evaluation of a complex NMT with bi-directional LSTMs, attention, and beam search. Our system scales well. Going from 1 to 8 FPGAs allows hosting ~8× larger model with only ~2× latency increase. A batch-1 inference for a 100M-parameter NMT on 8 Stratix 10 FPGAs takes only ~10 ms. This system offers 110× better latency than the only prior NMT work on FPGAs, which uses a high-end FPGA and stores the model off-chip.