# ElasticAI: Creating and Deploying Energy-Efficient Deep Learning Accelerator for Pervasive Computing

arXiv: https://arxiv.org/abs/2409.09044 · HTML: https://mlsanity.com/paper/2409.09044/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 29 Aug 2024.

## Builds on (most cited first, 3 of 3)

- [hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices](https://mlsanity.com/paper/2103.05579/) (2021)
- [FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks](https://mlsanity.com/paper/1809.04570/) (2018)
- [Enhancing Energy-efficiency by Solving the Throughput Bottleneck of LSTM Cells for Embedded FPGAs](https://mlsanity.com/paper/2310.16842/) (2023)

## Built on by (most cited first, 5 of 5)

- [Automating Versatile Time-Series Analysis with Tiny Transformers on Embedded FPGAs](https://mlsanity.com/paper/2505.17662/) (2025)
- [Automated Energy-Aware Time-Series Model Deployment on Embedded FPGAs for Resilient Combined Sewer Overflow Management](https://mlsanity.com/paper/2508.13905/) (2025)
- [Resource-aware Mixed-precision Quantization for Enhancing Deployability of Transformers for Time-series Forecasting on Embedded FPGAs](https://mlsanity.com/paper/2410.03294/) (2024)
- [Enabling Vibration-Based Gesture Recognition on Everyday Furniture via Energy-Efficient FPGA Implementation of 1D Convolutional Networks](https://mlsanity.com/paper/2510.23156/) (2025)
- [StrikeWatch: Wrist-worn Gait Recognition with Compact Time-series Models on Low-power FPGAs](https://mlsanity.com/paper/2510.24738/) (2025)

## Abstract (arXiv)

Deploying Deep Learning (DL) on embedded end devices is a scorching trend in pervasive computing. Since most Microcontrollers on embedded devices have limited computing power, it is necessary to add a DL accelerator. Embedded Field Programmable Gate Arrays (FPGAs) are suitable for deploying DL accelerators for embedded devices, but developing an energy-efficient DL accelerator on an FPGA is not easy. Therefore, we propose the ElasticAI-Workflow that aims to help DL developers to create and deploy DL models as hardware accelerators on embedded FPGAs. This workflow consists of two key components: the ElasticAI-Creator and the Elastic Node. The former is a toolchain for automatically generating DL accelerators on FPGAs. The latter is a hardware platform for verifying the performance of the generated accelerators. With this combination, the performance of the accelerator can be sufficiently guaranteed. We will demonstrate the potential of our approach through a case study.