# fpgaHART: A toolflow for throughput-oriented acceleration of 3D CNNs for HAR onto FPGAs

arXiv: https://arxiv.org/abs/2305.19896 · HTML: https://mlsanity.com/paper/2305.19896/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 4 of 4)

- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Omni-sourced Webly-supervised Learning for Video Recognition](https://mlsanity.com/paper/2003.13042/) (2020)
- [HARFLOW3D: A Latency-Oriented 3D-CNN Accelerator Toolflow for HAR on FPGA Devices](https://mlsanity.com/paper/2303.17218/) (2023)
- [FMM-X3D: FPGA-based modeling and mapping of X3D for Human Action Recognition](https://mlsanity.com/paper/2305.18479/) (2023)

## Built on by (most cited first, 2 of 2)

- [FPGA-based Acceleration for Convolutional Neural Networks: A Comprehensive Review](https://mlsanity.com/paper/2505.13461/) (2025)
- [SMOF: Streaming Modern CNNs on FPGAs with Smart Off-Chip Eviction](https://mlsanity.com/paper/2403.18921/) (2024)

## Abstract (arXiv)

Surveillance systems, autonomous vehicles, human monitoring systems, and video retrieval are just few of the many applications in which 3D Convolutional Neural Networks are exploited. However, their extensive use is restricted by their high computational and memory requirements, especially when integrated into systems with limited resources. This study proposes a toolflow that optimises the mapping of 3D CNN models for Human Action Recognition onto FPGA devices, taking into account FPGA resources and off-chip memory characteristics. The proposed system employs Synchronous Dataflow (SDF) graphs to model the designs and introduces transformations to expand and explore the design space, resulting in high-throughput designs. A variety of 3D CNN models were evaluated using the proposed toolflow on multiple FPGA devices, demonstrating its potential to deliver competitive performance compared to earlier hand-tuned and model-specific designs.