# Accelerating CNN inference on FPGAs: A Survey

arXiv: https://arxiv.org/abs/1806.01683 · HTML: https://mlsanity.com/paper/1806.01683/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2018.

## Built on by (most cited first, 16 of 16)

- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
- [hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices](https://mlsanity.com/paper/2103.05579/) (2021)
- [Fast convolutional neural networks on FPGAs with hls4ml](https://mlsanity.com/paper/2101.05108/) (2021)
- [DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators](https://mlsanity.com/paper/2303.08226/) (2023)
- [Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark](https://mlsanity.com/paper/2206.11791/) (2022)
- [Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Accelerators](https://mlsanity.com/paper/2401.09509/) (2024)
- [Learning on Hardware: A Tutorial on Neural Network Accelerators and Co-Processors](https://mlsanity.com/paper/2104.09252/) (2021)
- [Physical Side-Channel Attacks on Embedded Neural Networks: A Survey](https://mlsanity.com/paper/2110.11290/) (2021)
- [SoWaF: Shuffling of Weights and Feature Maps: A Novel Hardware Intrinsic Attack (HIA) on Convolutional Neural Network (CNN)](https://mlsanity.com/paper/2103.09327/) (2021)
- [H2PIPE: High throughput CNN Inference on FPGAs with High-Bandwidth Memory](https://mlsanity.com/paper/2408.09209/) (2024)
- [BearingPGA-Net: A Lightweight and Deployable Bearing Fault Diagnosis Network via Decoupled Knowledge Distillation and FPGA Acceleration](https://mlsanity.com/paper/2307.16363/) (2023)
- [FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review](https://mlsanity.com/paper/2506.03938/) (2025)
- [Attention, Distillation, and Tabularization: Towards Practical Neural Network-Based Prefetching](https://mlsanity.com/paper/2401.06362/) (2023)
- [TabConv: Low-Computation CNN Inference via Table Lookups](https://mlsanity.com/paper/2404.05872/) (2024)
- [Real-Time Semantic Segmentation of Aerial Images Using an Embedded U-Net: A Comparison of CPU, GPU, and FPGA Workflows](https://mlsanity.com/paper/2503.08700/) (2025)
- [Efficient Memory Partitioning in Software Defined Hardware](https://mlsanity.com/paper/2202.01261/) (2022)

## Abstract (arXiv)

Convolutional Neural Networks (CNNs) are currently adopted to solve an ever greater number of problems, ranging from speech recognition to image classification and segmentation. The large amount of processing required by CNNs calls for dedicated and tailored hardware support methods. Moreover, CNN workloads have a streaming nature, well suited to reconfigurable hardware architectures such as FPGAs. The amount and diversity of research on the subject of CNN FPGA acceleration within the last 3 years demonstrates the tremendous industrial and academic interest. This paper presents a state-of-the-art of CNN inference accelerators over FPGAs. The computational workloads, their parallelism and the involved memory accesses are analyzed. At the level of neurons, optimizations of the convolutional and fully connected layers are explained and the performances of the different methods compared. At the network level, approximate computing and datapath optimization methods are covered and state-of-the-art approaches compared. The methods and tools investigated in this survey represent the recent trends in FPGA CNN inference accelerators and will fuel the future advances on efficient hardware deep learning.