ImaGen: A General Framework for Generating Memory- and Power-Efficient Image Processing Accelerators

Author:

Ujjainkar Nisarg1ORCID,Leng Jingwen2ORCID,Zhu Yuhao3ORCID

Affiliation:

1. Computer Science, University of Rochester, Rochester, NY, USA

2. Shanghai Jiao Tong University, Shanghai, China

3. University of Rochester, Rochester, NY, USA

Publisher

ACM

Reference40 articles.

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2. [n. d.]. Google OR-Tools. https://developers.google.com/optimization. [n. d.]. Google OR-Tools. https://developers.google.com/optimization.

3. [n. d.]. Mali-C55. https://developer.arm.com/Processors/Mali-C55 . [n. d.]. Mali-C55. https://developer.arm.com/Processors/Mali-C55.

4. [n. d.]. Snapdragon Makes Significant Leap for Mobile Cameras with Qualcomm Spectra Image Signal Processor and Snapdragon Sight. https://futurumresearch.com/snapdragon-makes-significant-leap-for-mobile-cameras-with-qualcomm-spectra-image-signal-processor-and-snapdragon-sight/. [n. d.]. Snapdragon Makes Significant Leap for Mobile Cameras with Qualcomm Spectra Image Signal Processor and Snapdragon Sight. https://futurumresearch.com/snapdragon-makes-significant-leap-for-mobile-cameras-with-qualcomm-spectra-image-signal-processor-and-snapdragon-sight/.

5. Daniele Bagni , Pari Kannan , and Stephen Neuendorffer . 2017. Demystifying the Lucas-Kanade optical flow algorithm with Vivado HLS. Tech. note XAPP1300. Xilinx ( 2017 ). Daniele Bagni, Pari Kannan, and Stephen Neuendorffer. 2017. Demystifying the Lucas-Kanade optical flow algorithm with Vivado HLS. Tech. note XAPP1300. Xilinx (2017).

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1. δLTA: Decoupling Camera Sampling from Processing to Avoid Redundant Computations in the Vision Pipeline;56th Annual IEEE/ACM International Symposium on Microarchitecture;2023-10-28

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