# EdgeDRNN: Recurrent Neural Network Accelerator for Edge Inference

arXiv: https://arxiv.org/abs/2012.13600 · HTML: https://mlsanity.com/paper/2012.13600/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2020.

## Built on by (most cited first, 19 of 19)

- [AEGNN: Asynchronous Event-based Graph Neural Networks](https://mlsanity.com/paper/2203.17149/) (2022)
- [Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models](https://mlsanity.com/paper/2503.06027/) (2025)
- [Spartus: A 9.4 TOp/s FPGA-based LSTM Accelerator Exploiting Spatio-Temporal Sparsity](https://mlsanity.com/paper/2108.02297/) (2021)
- [Ditto: Accelerating Diffusion Model via Temporal Value Similarity](https://mlsanity.com/paper/2501.11211/) (2025)
- [RecLight: A Recurrent Neural Network Accelerator with Integrated Silicon Photonics](https://mlsanity.com/paper/2209.00084/) (2022)
- [To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Acceleration](https://mlsanity.com/paper/2306.15749/) (2023)
- [Automated Pest Detection with DNN on the Edge for Precision Agriculture](https://mlsanity.com/paper/2108.00421/) (2021)
- [DeltaLLM: Delta-Key Sparse Attention for Edge LLMs](https://mlsanity.com/paper/2507.19608/) (2025)
- [MP-DPD: Low-Complexity Mixed-Precision Neural Networks for Energy-Efficient Digital Predistortion of Wideband Power Amplifiers](https://mlsanity.com/paper/2404.15364/) (2024)
- [DeltaKWS: A 65nm 36nJ/Decision Bio-inspired Temporal-Sparsity-Aware Digital Keyword Spotting IC with 0.6V Near-Threshold SRAM](https://mlsanity.com/paper/2405.03905/) (2024)
- [Exploiting Symmetric Temporally Sparse BPTT for Efficient RNN Training](https://mlsanity.com/paper/2312.09391/) (2023)
- [Hardware Neural Control of CartPole and F1TENTH Race Car](https://mlsanity.com/paper/2407.08681/) (2024)
- [DPD-NeuralEngine: A 22-nm 6.6-TOPS/W/mm2 Recurrent Neural Network Accelerator for Wideband Power Amplifier Digital Pre-Distortion](https://mlsanity.com/paper/2410.11766/) (2024)
- [Ethical Fairness in Ubiquitous Health Sensing without Known Attributes](https://mlsanity.com/paper/2603.13373/) (2026)
- [Spiking Cochlea with System-level Local Automatic Gain Control](https://mlsanity.com/paper/2202.06707/) (2022)
- [Automated and Holistic Co-design of Neural Networks and ASICs for Enabling In-Pixel Intelligence](https://mlsanity.com/paper/2407.14560/) (2024)
- [Neural Network Acceleration on MPSoC board: Integrating SLAC's SNL, Rogue Software and Auto-SNL](https://mlsanity.com/paper/2508.21739/) (2025)
- [Architectural Design and Performance Analysis of FPGA based AI Accelerators: A Comprehensive Review](https://mlsanity.com/paper/2603.08740/) (2026)
- [RETRACE: Resilience-Guided Trait-Conditioned Craving Estimation from Wearable Physiology in Opioid Use Disorder](https://mlsanity.com/paper/2608.14947/) (2026)

## Abstract (arXiv)

Low-latency, low-power portable recurrent neural network (RNN) accelerators offer powerful inference capabilities for real-time applications such as IoT, robotics, and human-machine interaction. We propose a lightweight Gated Recurrent Unit (GRU)-based RNN accelerator called EdgeDRNN that is optimized for low-latency edge RNN inference with batch size of 1. EdgeDRNN adopts the spiking neural network inspired delta network algorithm to exploit temporal sparsity in RNNs. Weights are stored in inexpensive DRAM which enables EdgeDRNN to compute large multi-layer RNNs on the most inexpensive FPGA. The sparse updates reduce DRAM weight memory access by a factor of up to 10x and the delta can be varied dynamically to trade-off between latency and accuracy. EdgeDRNN updates a 5 million parameter 2-layer GRU-RNN in about 0.5ms. It achieves latency comparable with a 92W Nvidia 1080 GPU. It outperforms NVIDIA Jetson Nano, Jetson TX2 and Intel Neural Compute Stick 2 in latency by 5X. For a batch size of 1, EdgeDRNN achieves a mean effective throughput of 20.2GOp/s and a wall plug power efficiency that is over 4X higher than the commercial edge AI platforms.