The 2020 Embedded Deep Learning Object Detection Model Compression Competition for Traffic in Asian Countries

Tsai, Chia-Chi; Yang, Yong-Hsiang; Lin, Hung-Wei; Wu, Bo-Xun; Chang, En-Chih; Liu, Hung Yu; Lai, Jhih-Sheng; Chen, Po‐Yuan; Lin, Jiann-Horng; Chang, Jen Shuo; Wang, Li-Jen; Kuo, Ted T.; Hwang, Jenq‐Neng; Guo, Jiun-In

doi:10.1109/icmew46912.2020.9106010

Cited by 16 publications

(10 citation statements)

References 12 publications

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“…The proposed model is implemented with Pytorch and evaluated for object detection in the autonomous driving application using the IVS 3cls [32] dataset. IVS 3cls is a cityscape object detection dataset with about 11,000 samples, including 10,000 training images and 1,000 test images.…”

Section: Experimental Results a Results Of The Proposed Modelmentioning

confidence: 99%

Sparse Compressed Spiking Neural Network Accelerator for Object Detection

Lien

Chang

2022

IEEE Trans. Circuits Syst. I

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Spiking neural networks (SNNs), which are inspired by the human brain, have recently gained popularity due to their relatively simple and low-power hardware for transmitting binary spikes and highly sparse activation maps. However, because SNNs contain extra time dimension information, the SNN accelerator will require more buffers and take longer to infer, especially for the more difficult high-resolution object detection task. As a result, this paper proposes a sparse compressed spiking neural network accelerator that takes advantage of the high sparsity of activation maps and weights by utilizing the proposed gated oneto-all product for low power and highly parallel model execution. The experimental result of the neural network shows 71.5% mAP with mixed (1,3) time steps on the IVS 3cls dataset. The accelerator with the TSMC 28nm CMOS process can achieve 1024×576@29 frames per second processing when running at 500MHz with 35.88TOPS/W energy efficiency and 1.05mJ energy consumption per frame.

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Section: Experimental Results a Results Of The Proposed Modelmentioning

confidence: 99%

Sparse Compressed Spiking Neural Network Accelerator for Object Detection

Lien

Chang

2022

IEEE Trans. Circuits Syst. I

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“…The embedded deep learning object-detection model compression competition for traffic in Asian countries entitled the IEEE International Conference on Multimedia and Expo (ICME-2020) Grand Challenges (GC) PAIR competition focused on the object detection and recognition of objects using sensing technology in autonomous vehicles. The target of the competition was to successfully implement the designed model on an embedded system with low complexity and low model sizes [17]. The dataset used in the competition was an iVS dataset comprising data mostly from Asian countries that contain some of the harshest driving environments, such as crowded streets with scooters, bicycles, pedestrians and vehicles.…”

Section: Ieee Icme-2020 Gc Pair Competitionmentioning

confidence: 99%

iVS Dataset and ezLabel: A Dataset and a Data Annotation Tool for Deep Learning Based ADAS Applications

Shivanna

Guo

2022

Remote Sensing

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To overcome the limitations of standard datasets with data at a wide-variety of scales and captured in the various conditions necessary to train neural networks to yield efficient results in ADAS applications, this paper presents a self-built open-to-free-use ‘iVS dataset’ and a data annotation tool entitled ‘ezLabel’. The iVS dataset is comprised of various objects at different scales as seen in and around real driving environments. The data in the iVS dataset are collected by employing a camcorder in vehicles driving under different conditions, e.g., light, weather and traffic, and driving scenarios ranging from city traffic during peak and normal hours to freeway traffics during busy and normal conditions. Thus, the collected data are wide-ranging and captured all possible objects at various scales appearing in real-time driving situations. The data collected in order to build the dataset has to be annotated before use in training the CNNs and so this paper presents an open-to-free-use data annotation tool, ezLabel, for data annotation purposes as well.

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“…The baseline and proposed models are implemented using Pytorch and evaluated on the IVS 3cls [17] dataset for object detection. This dataset contains 10,000 training images and 1,000 test images with three categories of objects: vehicles, bike, and pedestrian.…”

Section: A Settingsmentioning

confidence: 99%

Hardware-Robust In-RRAM-Computing for Object Detection

Chiang

Sung

et al. 2022

IEEE J. Emerg. Sel. Topics Circuits Syst.

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In-memory computing is becoming a popular architecture for deep-learning hardware accelerators recently due to its highly parallel computing, low power, and low area cost. However, in-RRAM computing (IRC) suffered from large device variation and numerous nonideal effects in hardware. Although previous approaches including these effects in model training successfully improved variation tolerance, they only considered part of the nonideal effects and relatively simple classification tasks. This paper proposes a joint hardware and software optimization strategy to design a hardware-robust IRC macro for object detection. We lower the cell current by using a low word-line voltage to enable a complete convolution calculation in one operation that minimizes the impact of nonlinear addition. We also implement ternary weight mapping and remove batch normalization for better tolerance against device variation, sense amplifier variation, and IR drop problem. An extra bias is included to overcome the limitation of the current sensing range. The proposed approach has been successfully applied to a complex object detection task with only 3.85% mAP drop, whereas a naive design suffers catastrophic failure under these nonideal effects.

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The 2020 Embedded Deep Learning Object Detection Model Compression Competition for Traffic in Asian Countries

Cited by 16 publications

References 12 publications

Sparse Compressed Spiking Neural Network Accelerator for Object Detection

Sparse Compressed Spiking Neural Network Accelerator for Object Detection

iVS Dataset and ezLabel: A Dataset and a Data Annotation Tool for Deep Learning Based ADAS Applications

Hardware-Robust In-RRAM-Computing for Object Detection

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