Low Latency Implementations of CNN for Resource-Constrained IoT Devices
- Authors
- Mujtaba, Ahmed; Lee, Wai-Kong; Hwang, Seong Oun
- Issue Date
- Dec-2022
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Convolutional neural networks; Internet-of-Things; microcontrollers; tiny ML
- Citation
- IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, v.69, no.12, pp.5124 - 5128
- Journal Title
- IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
- Volume
- 69
- Number
- 12
- Start Page
- 5124
- End Page
- 5128
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/87089
- DOI
- 10.1109/TCSII.2022.3205029
- ISSN
- 1549-7747
- Abstract
- Convolutional Neural Network (CNN) inference on a resource-constrained Internet-of-Things (IoT) device (i.e., ARM Cortex-M microcontroller) requires careful optimization to reduce the timing overhead. We propose two novel techniques to improve the computational efficiency of CNNs by targeting low-cost microcontrollers. Our techniques utilize on-chip memory and minimize redundant operations, yielding low-latency inference results on complex quantized models such as MobileNetV1. On the ImageNet dataset for per-layer quantization, we reduce inference latency and Multiply-and-Accumulate (MAC) per cycle by 22.4% and 22.9%, respectively, compared to the state-of-theart mixed-precision CMix-NN library. On the CIFAR-10 dataset for per-channel quantization, we reduce inference latency and MAC per cycle by 31.7% and 31.3%, respectively. The achieved low-latency inference results can improve the user experience and save power budget in resource-constrained IoT devices.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - IT융합대학 > 컴퓨터공학과 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/87089)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.