In-pixel AI for lossy data compression at source for X-ray detectors

B. Valentin, Manuel; Di Guglielmo, Giuseppe; Noonan, Danny; Dilip, Priyanka; Huang, Panpan; Quinn, Adam; Zimmerman, Thomas; Braga, Davide; Ogrenci, Seda; Jacobsen, Chris; Tran, Nhan; Fahim, Farah

doi:10.1016/j.nima.2023.168665

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On-chip Data Reduction1

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2024

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“…More recently, lossy on-chip compression methods have been developed using two different algorithms -principal components analysis and an autoencoder [74]. The logic for doing this was distributed throughout the chip, with the analog pixel circuitry consisting of "islands" of 2 × 2 pixels.…”

Section: On-chip Data Reductionmentioning

confidence: 99%

Data reduction and processing for photon science detectors

Pennicard,

Rahmani,

Graafsma

2024

Front. Phys.

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New detectors in photon science experiments produce rapidly-growing volumes of data. For detector developers, this poses two challenges; firstly, raw data streams from detectors must be converted to meaningful images at ever-higher rates, and secondly, there is an increasing need for data reduction relatively early in the data processing chain. An overview of data correction and reduction is presented, with an emphasis on how different data reduction methods apply to different experiments in photon science. These methods can be implemented in different hardware (e.g., CPU, GPU or FPGA) and in different stages of a detector’s data acquisition chain; the strengths and weaknesses of these different approaches are discussed.

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Section: On-chip Data Reductionmentioning

confidence: 99%