# DFX: A Low-latency Multi-FPGA Appliance for Accelerating Transformer-based Text Generation

arXiv: https://arxiv.org/abs/2209.10797 · HTML: https://mlsanity.com/paper/2209.10797/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 53 references that are in the map.
- First posted on arXiv 22 Sep 2022.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [BAE: BERT-based Adversarial Examples for Text Classification](https://mlsanity.com/paper/2004.01970/) (2020)
- [Learning Deep Transformer Models for Machine Translation](https://mlsanity.com/paper/1906.01787/) (2019)
- [SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning](https://mlsanity.com/paper/2012.09852/) (2020)
- [TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents](https://mlsanity.com/paper/1901.08149/) (2019)
- [Mesh-TensorFlow: Deep Learning for Supercomputers](https://mlsanity.com/paper/1811.02084/) (2018)
- [Hello, It's GPT-2 -- How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1907.05774/) (2019)
- [GOBO: Quantizing Attention-Based NLP Models for Low Latency and Energy Efficient Inference](https://mlsanity.com/paper/2005.03842/) (2020)
- [A3: Accelerating Attention Mechanisms in Neural Networks with Approximation](https://mlsanity.com/paper/2002.10941/) (2020)
- [EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference](https://mlsanity.com/paper/2011.14203/) (2020)
- [A Plug-and-Play Method for Controlled Text Generation](https://mlsanity.com/paper/2109.09707/) (2021)
- [Comparing BERT against traditional machine learning text classification](https://mlsanity.com/paper/2005.13012/) (2020)
- [Evaluating Modern GPU Interconnect: PCIe, NVLink, NV-SLI, NVSwitch and GPUDirect](https://mlsanity.com/paper/1903.04611/) (2019)
- [Efficient Algorithms for Device Placement of DNN Graph Operators](https://mlsanity.com/paper/2006.16423/) (2020)
- [Embracing data abundance: BookTest Dataset for Reading Comprehension](https://mlsanity.com/paper/1610.00956/) (2016)
- [A Sentiment-Controllable Topic-to-Essay Generator with Topic Knowledge Graph](https://mlsanity.com/paper/2010.05511/) (2020)
- [First-Generation Inference Accelerator Deployment at Facebook](https://mlsanity.com/paper/2107.04140/) (2021)

## Built on by (most cited first, 30 of 48)

- [QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving](https://mlsanity.com/paper/2405.04532/) (2024)
- [A Survey on Efficient Inference for Large Language Models](https://mlsanity.com/paper/2404.14294/) (2024)
- [LLM Inference Unveiled: Survey and Roofline Model Insights](https://mlsanity.com/paper/2402.16363/) (2024)
- [Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective](https://mlsanity.com/paper/2410.04466/) (2024)
- [A Survey of Techniques for Optimizing Transformer Inference](https://mlsanity.com/paper/2307.07982/) (2023)
- [Duplex: A Device for Large Language Models with Mixture of Experts, Grouped Query Attention, and Continuous Batching](https://mlsanity.com/paper/2409.01141/) (2024)
- [Understanding the Potential of FPGA-Based Spatial Acceleration for Large Language Model Inference](https://mlsanity.com/paper/2312.15159/) (2023)
- [LLMServingSim: A HW/SW Co-Simulation Infrastructure for LLM Inference Serving at Scale](https://mlsanity.com/paper/2408.05499/) (2024)
- [Tender: Accelerating Large Language Models via Tensor Decomposition and Runtime Requantization](https://mlsanity.com/paper/2406.12930/) (2024)
- [A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models](https://mlsanity.com/paper/2410.07265/) (2024)
- [Scaling Down to Scale Up: A Cost-Benefit Analysis of Replacing OpenAI's LLM with Open Source SLMs in Production](https://mlsanity.com/paper/2312.14972/) (2023)
- [PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing System](https://mlsanity.com/paper/2502.15470/) (2025)
- [A Survey on Hardware Accelerators for Large Language Models](https://mlsanity.com/paper/2401.09890/) (2024)
- [Allo: A Programming Model for Composable Accelerator Design](https://mlsanity.com/paper/2404.04815/) (2024)
- [ConSmax: Hardware-Friendly Alternative Softmax with Learnable Parameters](https://mlsanity.com/paper/2402.10930/) (2024)
- [LightMamba: Efficient Mamba Acceleration on FPGA with Quantization and Hardware Co-design](https://mlsanity.com/paper/2502.15260/) (2025)
- [PTQTP: Post-Training Quantization to Trit-Planes for Large Language Models](https://mlsanity.com/paper/2509.16989/) (2025)
- [LLMServingSim 2.0: A Unified Simulator for Heterogeneous and Disaggregated LLM Serving Infrastructure](https://mlsanity.com/paper/2602.23036/) (2026)
- [New Solutions on LLM Acceleration, Optimization, and Application](https://mlsanity.com/paper/2406.10903/) (2024)
- [Pushing up to the Limit of Memory Bandwidth and Capacity Utilization for Efficient LLM Decoding on Embedded FPGA](https://mlsanity.com/paper/2502.10659/) (2025)
- [Ultra Fast Transformers on FPGAs for Particle Physics Experiments](https://mlsanity.com/paper/2402.01047/) (2024)
- [TerEffic: Highly Efficient Ternary LLM Inference on FPGA](https://mlsanity.com/paper/2502.16473/) (2025)
- [Low Latency Transformer Inference on FPGAs for Physics Applications with hls4ml](https://mlsanity.com/paper/2409.05207/) (2024)
- [PIMphony: Overcoming Bandwidth and Capacity Inefficiency in PIM-based Long-Context LLM Inference System](https://mlsanity.com/paper/2412.20166/) (2024)
- [V-Rex: Real-Time Streaming Video LLM Acceleration via Dynamic KV Cache Retrieval](https://mlsanity.com/paper/2512.12284/) (2025)
- [Achieving Peak Performance for Large Language Models: A Systematic Review](https://mlsanity.com/paper/2409.04833/) (2024)
- [Deploying Foundation Model Powered Agent Services: A Survey](https://mlsanity.com/paper/2412.13437/) (2024)
- [A Cost-Effective Near-Storage Processing Solution for Offline Inference of Long-Context LLMs](https://mlsanity.com/paper/2502.09921/) (2025)
- [ADOR: A Design Exploration Framework for LLM Serving with Enhanced Latency and Throughput](https://mlsanity.com/paper/2503.04253/) (2025)
- [MCBP: A Memory-Compute Efficient LLM Inference Accelerator Leveraging Bit-Slice-enabled Sparsity and Repetitiveness](https://mlsanity.com/paper/2509.10372/) (2025)

## Abstract (arXiv)

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.