ASNet: Introducing Approximate Hardware to High-Level Synthesis of Neural Networks

Froehlich, Saman; Klemmer, Lucas; Grose, Daniel; Drechsler, Rolf

doi:10.1109/ismvl49045.2020.00-28

Cited by 2 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The algorithms involved in the comparison include the two-stage baseline model Faster RCNN with FPN, and the single-stage baseline models YOLOv5s, YOLOv5m, and YOLOv5l. Moreover, the popular fine-grained domain-objectdetection algorithms NDFT-DE-FPN [11] , PG-YOLO [36], ASNet [37], and Kiefer et al [13] are also added to the comparison. NDFT-DE-FPN is based on the best-performing single model reported in the leaderboard [38], which utilized FPN with a ResNeXt-101 64-4d backbone, and then the finegrained domain learning module NDFT proposed by [11] is added on it.…”

Section: Visdronementioning

confidence: 99%

Domain Feature Decomposition for Efficient Object Detection in Aerial Images

Jin,

Jia,

Yin

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Object detection in UAV aerial images faces domain-adaptive challenges, such as changes in shooting height, viewing angle, and weather. These changes constitute a large number of fine-grained domains that place greater demands on the network’s generalizability. To tackle these challenges, we initially decompose image features into domain-invariant and domain-specific features using practical imaging condition parameters. The composite feature can improve domain generalization and single-domain accuracy compared to the conventional fine-grained domain-detection method. Then, to solve the problem of the overfitting of high-frequency imaging condition parameters, we mixed images from different imaging conditions in a balanced sampling manner as input for the training of the detection network. The data-augmentation method improves the robustness of training and reduces the overfitting of high-frequency imaging parameters. The proposed algorithm is compared with state-of-the-art fine-grained domain detectors on the UAVDT and VisDrone datasets. The results show that it achieves an average detection precision improvement of 5.7 and 2.4, respectively. The airborne experiments validate that the algorithm achieves a 20 Hz processing performance for 720P images on an onboard computer with Nvidia Jetson Xavier NX.

show abstract

Section: Visdronementioning

confidence: 99%

Domain Feature Decomposition for Efficient Object Detection in Aerial Images

Jin,

Jia,

Yin

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Integrating given approximate components (e.g. adders, multipliers [20]- [22]) into LeFlow has been considered in [23]. In [24], the authors proposed an approximation resilience exploration metric and an approximate accelerator for convolutional layers targeted for edge devices.…”

Section: Related Workmentioning

confidence: 99%

XbNN: Enabling CNNs on Edge Devices by Approximate On-Chip Dot Product Encoding

Klemmer

Froehlich

Drechsler

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

Self Cite

View full text Add to dashboard Cite

Only a few trends have gained as much traction as Edge Computing and Neural Networks (NN). Both have the potential to radically change how technology influences us. However, since edge devices feature only very limited resources, the sheer amount of performance required by modern NNs limits their use on the edge. Especially, the conversion of Convolutional Neural Networks (CNN) into feasible on-chip designs remains a hard task. Currently, hand-crafted and most-often very heavy architectures have to be used as existing High-Level Synthesis (HLS) frameworks provide only inefficient solutions.In this paper, we introduce the Crossbar Neural Network (XbNN) architecture. Our architecture employs a novel approximate on-chip dot product encoding for the efficient synthesis of CNNs on hardware. This encoding embeds the weights used in CNNs into the hardware design itself, significantly reducing the required memory and computation time. In addition, we present a methodology for the automated conversion of traditional CNNs given in TensorFlow into accelerators on top of the XbNN architecture. To demonstrate the effectiveness of XbNN, we conduct experiments on a common CNN test dataset and analyze the accuracy and performance of the resulting XbNN accelerators. We show that XbNN (a) achieves similar accuracies compared to TensorFlow CNNs and (b) provides much better area and performance results in comparison to a state-of-the-art HLS flow.

show abstract

ASNet: Introducing Approximate Hardware to High-Level Synthesis of Neural Networks

Cited by 2 publications

References 18 publications

Domain Feature Decomposition for Efficient Object Detection in Aerial Images

Domain Feature Decomposition for Efficient Object Detection in Aerial Images

XbNN: Enabling CNNs on Edge Devices by Approximate On-Chip Dot Product Encoding

Contact Info

Product

Resources

About