2026-03-23 アルゴンヌ国立研究所（ANL）
Silicon chip that integrates both imaging sensors and data compression, shown next to a U.S. penny and resting on grains of sand. This chip was co-designed by Argonne and SLAC. (Image by Antonino Miceli/Argonne National Laboratory.)
＜関連情報＞
- https://www.anl.gov/article/new-chip-technology-enables-realtime-insights-from-scientific-data
- https://iopscience.iop.org/article/10.1088/1748-0221/20/10/P10027
- https://opg.optica.org/oe/fulltext.cfm?uri=oe-33-5-12059
MHzフレームレートのX線および電子ピクセル検出器におけるオンチップデータ圧縮のための28nmマルチプライアキュムレータASICアーキテクチャ A 28 nm multiply-accumulate ASIC architecture for on-chip data compression in MHz frame rate X-ray and electron pixel detectors
Rami Rasheedi, Nicholas Contini, Mohamed Adel Gharib, Sebastian Strempfer, Senthil Gnanasekaran, Salma Abdelzaher, Tejas Guruswamy, Kazutomo Yoshii, Mike Hammer, Henry Shi,…
Journal of Instrumentation Published: 10 October 2025
DOI:10.1088/1748-0221/20/10/P10027
Abstract
Modern X-ray detector systems urgently require compact, efficient, and fast data compression schemes to handle the transmission of big data from pixel arrays, enabling frame rates in the MHz regime. In this work, a data compression ASIC that implements a streaming fixed-length lossy compression scheme is introduced and analyzed, proving the feasibility and benefits of on-chip compression. The compression scheme utilizes a vector matrix product logic, which performs a number of floating-point multiplications, additions, and accumulations. The logic is verified, synthesized, and shown to fit in the area resource available for the X-ray detector under study, which comprises 192 × 168 pixels each of 12-bit width, and having a total area of 20 mm× 20 mm, about 2 mm× 20 mm of which are available for the digital logic. Several system architectures, precisions, and compression ratios ranging from 100 to 250 were analyzed to pave the way for on-chip fixed-length compression (e.g., principal component analysis, singular value decomposition) and data reduction (e.g., azimuthal integration) for X-ray and electron detectors.
リアルタイム光子相関解析のための準同型データ圧縮 Homomorphic data compression for real time photon correlation analysis
Sebastian Strempfer, Zichao Wendy Di, Kazutomo Yoshii, Yue Cao, Qingteng Zhang, Eric M. Dufresne, Mathew Cherukara, Suresh Narayanan, Martin V. Holt, Antonino Miceli, and Tao Zhou
Optics Express Published: March 7, 2025
DOI:https://doi.org/10.1364/OE.543404
Abstract
The construction of highly coherent X-ray sources, combined with next-generation detectors that are larger and faster, has enabled new research opportunities across the scientific landscape. Among the techniques that benefit most from these advancements is X-ray photon correlation spectroscopy (XPCS), where faster acquisition unlocks the ability to study faster dynamics within samples. However, faster acquisition on larger detectors also introduces unprecedented challenges for online data processing and offline data storage. Such challenges are particularly prominent for XPCS, where real time analyses require simultaneous calculation of all the previously acquired data in the time series. We present a homomorphic compression scheme to effectively reduce the computational time and memory space required for XPCS analysis. Leveraging similarities in the mathematical expression between a matrix-based compression algorithm and the correlation calculation, our approach allows direct operation on the compressed data without their decompression. The offline compression scheme extends storage capacity by a factor of 40 while preserving key features in the lossy compressed data. Meanwhile, the online compression scheme reduces the computational time to below 1 ms, enabling real time calculation of the correlation functions at kHz framerate. Our demonstration of a homomorphic compression of scientific data provides an effective solution to the big data challenge at coherent light sources. Beyond the example shown in this work, the framework can be extended to facilitate real-time operations directly on a compressed data stream for other techniques.
