BED: A Real-Time Object Detection System for Edge Devices
Guanchu Wang, Zaid Pervaiz Bhat, Zhimeng Jiang, Yi-Wei Chen, and Daochen Zha, Alfredo Costilla Reyes, Afshin Niktash, Gorkem, Ulkar, Erman Okman, Xuanting Cai, Xia Hu

TL;DR
BED is a real-time object detection system optimized for edge devices, combining a tiny DNN model with efficient inference on MAX78000 hardware, enabling accurate detection with low latency and energy consumption.
Contribution
This paper introduces BED, a complete, open-source object detection system for edge devices that integrates model training, quantization, and deployment on a specialized DNN accelerator.
Findings
Achieves 91.9 ms inference time with a 300-KB model
Consumes only 1.845 mJ of energy per inference
Provides accurate detection on resource-constrained hardware
Abstract
Deploying deep neural networks~(DNNs) on edge devices provides efficient and effective solutions for the real-world tasks. Edge devices have been used for collecting a large volume of data efficiently in different domains. DNNs have been an effective tool for data processing and analysis. However, designing DNNs on edge devices is challenging due to the limited computational resources and memory. To tackle this challenge, we demonstrate Object Detection System for Edge Devices~(BED) on the MAX78000 DNN accelerator. It integrates on-device DNN inference with a camera and an LCD display for image acquisition and detection exhibition, respectively. BED is a concise, effective and detailed solution, including model training, quantization, synthesis and deployment. The entire repository is open-sourced on Github, including a Graphical User Interface~(GUI) for on-chip debugging. Experiment…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Neural Network Applications
