# A Reconfigurable Streaming Deep Convolutional Neural Network Accelerator for Internet of Things

arXiv: https://arxiv.org/abs/1707.02973 · HTML: https://mlsanity.com/paper/1707.02973/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2017.

## Built on by (most cited first, 6 of 6)

- [Lightweight Deep Learning for Resource-Constrained Environments: A Survey](https://mlsanity.com/paper/2404.07236/) (2024)
- [A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms](https://mlsanity.com/paper/2306.15552/) (2023)
- [Anomaly Detection using Edge Computing in Video Surveillance System: Review](https://mlsanity.com/paper/2107.02778/) (2021)
- [Memory-Efficient CNN Accelerator Based on Interlayer Feature Map Compression](https://mlsanity.com/paper/2110.06155/) (2021)
- [A Column Streaming-Based Convolution Engine and Mapping Algorithm for CNN-based Edge AI accelerators](https://mlsanity.com/paper/2109.07601/) (2021)
- [Detection of Small Targets in Sea Clutter Based on RepVGG and Continuous Wavelet Transform](https://mlsanity.com/paper/2311.07912/) (2023)

## Abstract (arXiv)

Convolutional neural network (CNN) offers significant accuracy in image detection. To implement image detection using CNN in the internet of things (IoT) devices, a streaming hardware accelerator is proposed. The proposed accelerator optimizes the energy efficiency by avoiding unnecessary data movement. With unique filter decomposition technique, the accelerator can support arbitrary convolution window size. In addition, max pooling function can be computed in parallel with convolution by using separate pooling unit, thus achieving throughput improvement. A prototype accelerator was implemented in TSMC 65nm technology with a core size of 5mm2. The accelerator can support major CNNs and achieve 152GOPS peak throughput and 434GOPS/W energy efficiency at 350mW, making it a promising hardware accelerator for intelligent IoT devices.