HOGEye: Neural Approximation of HOG Feature Extraction in RRAM-Based 3D-Stacked Image Sensors-Reference-Cited by-同舟云学术

HOGEye: Neural Approximation of HOG Feature Extraction in RRAM-Based 3D-Stacked Image Sensors

Published:2022-08 Issue: Volume: Page:
ISSN:
Container-title:ACM/IEEE International Symposium on Low Power Electronics and Design
language:
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Author:

Ma Tianrui¹,Cao Weidong¹,Qiao Fei²,Chakrabarti Ayan¹,Zhang Xuan¹

Affiliation:

1. Washington University in St. Louis, United States

2. Tsinghua University, China

Funder

NSF

Publisher

ACM

Reference28 articles.

1. Tim Brooks Ben Mildenhall Tianfan Xue Jiawen Chen Dillon Sharlet and Jonathan T. Barron. 2018. Unprocessing Images for Learned Raw Denoising. CoRR abs/1811.11127(2018). arXiv:1811.11127 Tim Brooks Ben Mildenhall Tianfan Xue Jiawen Chen Dillon Sharlet and Jonathan T. Barron. 2018. Unprocessing Images for Learned Raw Denoising. CoRR abs/1811.11127(2018). arXiv:1811.11127

2. Weidong Cao Xin He Ayan Chakrabarti and Xuan Zhang. 2019. NeuADC: Neural Network-Inspired RRAM-Based Synthesizable Analog-to-Digital Conversion with Reconfigurable Quantization Support. In DATE’19. 1477–1482. Weidong Cao Xin He Ayan Chakrabarti and Xuan Zhang. 2019. NeuADC: Neural Network-Inspired RRAM-Based Synthesizable Analog-to-Digital Conversion with Reconfigurable Quantization Support. In DATE’19. 1477–1482.

3. NeuADC: Neural Network-Inspired Synthesizable Analog-to-Digital Conversion;Cao Weidong;IEEE TCAD,2020

4. Weidong Cao , Liu Ke , Ayan Chakrabarti , and Xuan Zhang . 2019. Neural Network-Inspired Analog-to-Digital Conversion to Achieve Super-Resolution with Low-Precision RRAM Devices . In IEEE/ACM ICCAD’ 19. 1–7. Weidong Cao, Liu Ke, Ayan Chakrabarti, and Xuan Zhang. 2019. Neural Network-Inspired Analog-to-Digital Conversion to Achieve Super-Resolution with Low-Precision RRAM Devices. In IEEE/ACM ICCAD’19. 1–7.

5. Weidong Cao , Yilong Zhao , Adith Boloor , Yinhe Han , Xuan Zhang , and Li Jiang . 2021. Neural-PIM: Efficient Processing-In-Memory with Neural Approximation of Peripherals . IEEE Trans. Comput . ( 2021 ), 1–1. Weidong Cao, Yilong Zhao, Adith Boloor, Yinhe Han, Xuan Zhang, and Li Jiang. 2021. Neural-PIM: Efficient Processing-In-Memory with Neural Approximation of Peripherals. IEEE Trans. Comput. (2021), 1–1.

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