AppCiP: Energy-Efficient Approximate Convolution-in-Pixel Scheme for Neural Network Acceleration

Tabrizchi, Sepehr; Nezhadi, Ali; Angizi, Shaahin; Roohi, Arman

doi:10.1109/jetcas.2023.3242167

Cited by 14 publications

(2 citation statements)

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“…In addition, CMOS sensors are much cheaper than CCD sensors, making them more cost efficient while achieving matching performance. The latest trend is smart CMOS technology to enable edge computing and neural networks on CMOS sensors [22,23]; see Section 2.3 for more details.…”

Section: Radiation Detectors and Imaging For Photon Sciencementioning

confidence: 99%

“…One solution is to utilize inpixel processing to directly extract features of the input pixels which can significantly reduce system bandwidth and power consumption of data transmission, memory management, and downstream data processing. In recent years, a number of works have been proposed to implement image sensors with in-pixel neural network processing; see [22,23] and references therein. This motivates real-time image processing for image sensors for various image processing tasks including noise removal.…”

Section: Real Time In-pixel Data-processingmentioning

confidence: 99%

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Neural network methods for radiation detectors and imaging

Lin,

Ning,

Zhu

et al. 2024

Front. Phys.

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Recent advances in image data proccesing through deep learning allow for new optimization and performance-enhancement schemes for radiation detectors and imaging hardware. This enables radiation experiments, which includes photon sciences in synchrotron and X-ray free electron lasers as a subclass, through data-endowed artificial intelligence. We give an overview of data generation at photon sources, deep learning-based methods for image processing tasks, and hardware solutions for deep learning acceleration. Most existing deep learning approaches are trained offline, typically using large amounts of computational resources. However, once trained, DNNs can achieve fast inference speeds and can be deployed to edge devices. A new trend is edge computing with less energy consumption (hundreds of watts or less) and real-time analysis potential. While popularly used for edge computing, electronic-based hardware accelerators ranging from general purpose processors such as central processing units (CPUs) to application-specific integrated circuits (ASICs) are constantly reaching performance limits in latency, energy consumption, and other physical constraints. These limits give rise to next-generation analog neuromorhpic hardware platforms, such as optical neural networks (ONNs), for high parallel, low latency, and low energy computing to boost deep learning acceleration (LA-UR-23-32395).

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Section: Radiation Detectors and Imaging For Photon Sciencementioning

confidence: 99%

Section: Real Time In-pixel Data-processingmentioning

confidence: 99%