# Understanding the Potential of FPGA-Based Spatial Acceleration for Large Language Model Inference

arXiv: https://arxiv.org/abs/2312.15159 · HTML: https://mlsanity.com/paper/2312.15159/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 59 of its 106 references that are in the map.
- First posted on arXiv 23 Dec 2023.

## Builds on (most cited first, 30 of 59)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models](https://mlsanity.com/paper/2211.10438/) (2022)
- [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://mlsanity.com/paper/1910.02054/) (2019)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [Accelerating Large Language Model Decoding with Speculative Sampling](https://mlsanity.com/paper/2302.01318/) (2023)
- [The LAMBADA dataset: Word prediction requiring a broad discourse context](https://mlsanity.com/paper/1606.06031/) (2016)
- [On Layer Normalization in the Transformer Architecture](https://mlsanity.com/paper/2002.04745/) (2020)
- [LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale](https://mlsanity.com/paper/2208.07339/) (2022)
- [CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://mlsanity.com/paper/2203.13474/) (2022)
- [Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM](https://mlsanity.com/paper/2104.04473/) (2021)
- [Efficiently Scaling Transformer Inference](https://mlsanity.com/paper/2211.05102/) (2022)
- [ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers](https://mlsanity.com/paper/2206.01861/) (2022)

## Built on by (most cited first, 30 of 33)

- [A Survey on Efficient Inference for Large Language Models](https://mlsanity.com/paper/2404.14294/) (2024)
- [FastDecode: High-Throughput GPU-Efficient LLM Serving using Heterogeneous Pipelines](https://mlsanity.com/paper/2403.11421/) (2024)
- [Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective](https://mlsanity.com/paper/2410.04466/) (2024)
- [Harmony in Divergence: Towards Fast, Accurate, and Memory-efficient Zeroth-order LLM Fine-tuning](https://mlsanity.com/paper/2502.03304/) (2025)
- [Allo: A Programming Model for Composable Accelerator Design](https://mlsanity.com/paper/2404.04815/) (2024)
- [LightMamba: Efficient Mamba Acceleration on FPGA with Quantization and Hardware Co-design](https://mlsanity.com/paper/2502.15260/) (2025)
- [Towards Unifying Understanding and Generation in the Era of Vision Foundation Models: A Survey from the Autoregression Perspective](https://mlsanity.com/paper/2410.22217/) (2024)
- [TeLLMe: An Energy-Efficient Ternary LLM Accelerator for Prefilling and Decoding on Edge FPGAs](https://mlsanity.com/paper/2504.16266/) (2025)
- [HG-PIPE: Vision Transformer Acceleration with Hybrid-Grained Pipeline](https://mlsanity.com/paper/2407.17879/) (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)
- [TerEffic: Highly Efficient Ternary LLM Inference on FPGA](https://mlsanity.com/paper/2502.16473/) (2025)
- [On-Device Qwen2.5: Efficient LLM Inference with Model Compression and Hardware Acceleration](https://mlsanity.com/paper/2504.17376/) (2025)
- [Tawa: Automatic Warp Specialization for Modern GPUs with Asynchronous References](https://mlsanity.com/paper/2510.14719/) (2025)
- [Dato: A Task-Based Programming Model for Dataflow Accelerators](https://mlsanity.com/paper/2509.06794/) (2025)
- [A Cost-Effective Near-Storage Processing Solution for Offline Inference of Long-Context LLMs](https://mlsanity.com/paper/2502.09921/) (2025)
- [LUT-LLM: Efficient Large Language Model Inference with Memory-based Computations on FPGAs](https://mlsanity.com/paper/2511.06174/) (2025)
- [TATAA: Programmable Mixed-Precision Transformer Acceleration with a Transformable Arithmetic Architecture](https://mlsanity.com/paper/2411.03697/) (2024)
- [A Runtime-Adaptive Transformer Neural Network Accelerator on FPGAs](https://mlsanity.com/paper/2411.18148/) (2024)
- [InTAR: Inter-Task Auto-Reconfigurable Accelerator Design for High Data Volume Variation in DNNs](https://mlsanity.com/paper/2502.08807/) (2025)
- [End-to-End On-Device Quantization-Aware Training for LLMs at Inference Cost](https://mlsanity.com/paper/2509.00031/) (2025)
- [FlexLLM: Composable HLS Library for Flexible Hybrid LLM Accelerator Design](https://mlsanity.com/paper/2601.15710/) (2026)
- [A Persistent-State Dataflow Accelerator for Memory-Bound Linear Attention Decode on FPGA](https://mlsanity.com/paper/2603.05931/) (2026)
- [LUTMUL: Exceed Conventional FPGA Roofline Limit by LUT-based Efficient Multiplication for Neural Network Inference](https://mlsanity.com/paper/2411.11852/) (2024)
- [NSFlow: An End-to-End FPGA Framework with Scalable Dataflow Architecture for Neuro-Symbolic AI](https://mlsanity.com/paper/2504.19323/) (2025)
- [ITERA-LLM: Boosting Sub-8-Bit Large Language Model Inference via Iterative Tensor Decomposition](https://mlsanity.com/paper/2505.08981/) (2025)
- [PD-Swap: Prefill-Decode Logic Swapping for End-to-End LLM Inference on Edge FPGAs via Dynamic Partial Reconfiguration](https://mlsanity.com/paper/2512.11550/) (2025)
- [The Feasibility of Implementing Large-Scale Transformers on Multi-FPGA Platforms](https://mlsanity.com/paper/2404.16158/) (2024)
- [LoopLynx: A Scalable Dataflow Architecture for Efficient LLM Inference](https://mlsanity.com/paper/2504.09561/) (2025)
- [Energy Efficient Software Hardware CoDesign for Machine Learning: From TinyML to Large Language Models](https://mlsanity.com/paper/2603.23668/) (2026)
- [Understand and Accelerate Memory Processing Pipeline for Large Language Model Inference](https://mlsanity.com/paper/2603.29002/) (2026)

## Abstract (arXiv)

Recent advancements in large language models (LLMs) boasting billions of parameters have generated a significant demand for efficient deployment in inference workloads. The majority of existing approaches rely on temporal architectures that reuse hardware units for different network layers and operators. However, these methods often encounter challenges in achieving low latency due to considerable memory access overhead. This paper investigates the feasibility and potential of model-specific spatial acceleration for LLM inference on FPGAs. Our approach involves the specialization of distinct hardware units for specific operators or layers, facilitating direct communication between them through a dataflow architecture while minimizing off-chip memory accesses. We introduce a comprehensive analytical model for estimating the performance of a spatial LLM accelerator, taking into account the on-chip compute and memory resources available on an FPGA. Through our analysis, we can determine the scenarios in which FPGA-based spatial acceleration can outperform its GPU-based counterpart. To enable more productive implementations of an LLM model on FPGAs, we further provide a library of high-level synthesis (HLS) kernels that are composable and reusable. This library will be made available as open-source. To validate the effectiveness of both our analytical model and HLS library, we have implemented BERT and GPT2 on an AMD Alveo U280 FPGA device. Experimental results demonstrate our approach can achieve up to 13.4x speedup when compared to previous FPGA-based accelerators for the BERT model. For GPT generative inference, we attain a 2.2x speedup compared to DFX, an FPGA overlay, in the prefill stage, while achieving a 1.9x speedup and a 5.7x improvement in energy efficiency compared to the NVIDIA A100 GPU in the decode stage.