Embedded field programmable gate array (eFPGA) technology allows the
implementation of reconfigurable logic within the design of an
application-specific integrated circuit (ASIC). This approach offers the low
power and efficiency of an ASIC along with the ease of FPGA configuration,
particularly beneficial for the use case of machine learning in the data
pipeline of next-generation collider experiments. An open-source framework
called "FABulous" was used to design eFPGAs using 130 nm and 28 nm CMOS
technology nodes, which were subsequently fabricated and verified through
testing. The capability of an eFPGA to act as a front-end readout chip was
tested using simulation of high energy particles passing through a silicon
pixel sensor. A machine learning-based classifier, designed for reduction of
sensor data at the source, was synthesized and configured onto the eFPGA. A
successful proof-of-concept was demonstrated through reproduction of the
expected algorithm result on the eFPGA with perfect accuracy. Further
development of the eFPGA technology and its application to collider detector
readout is discussed.

使用嵌入式可编程门阵列（eFPGA）技术在应用特定集成电路（ASIC）的设计中实现可重构逻辑，将 ASIC 的低功耗和高效性与 FPGA 的易配置性相结合，尤其适用于下一代对撞机实验数据流中的机器学习用例。通过开源框架 “FABulous”，设计了使用 130 纳米和 28 纳米 CMOS 技术节点的 eFPGA，并经过测试进行了验证。通过模拟高能粒子经过硅像素传感器和基于机器学习的分类器的测试，验证了 eFPGA 作为前端读出芯片的能力。通过在 eFPGA 上成功合成和配置传感器数据的降维算法，以完美准确度复现了预期算法结果。讨论了对 eFPGA 技术的进一步发展以及其在对撞机探测器读出中的应用。

在粒子探测器读出中，130 纳米和 28 纳米 CMOS 嵌入式 FPGA 的机器学习发展

Embedded FPGA Developments in 130nm and 28nm CMOS for Machine Learning  in Particle Detector Readout

In modern collider experiments, the quest to explore fundamental interactions
between elementary particles has reached unparalleled levels of precision.
Signatures from particle physics detectors are low-level objects encoding the
physics of collisions. The complete simulation of them in a detector is a
memory and storage-intensive task. To address this computational bottleneck in
particle physics, "Fast Simulation" has been introduced and refined over the
years. The field has seen a surge in interest in surrogate modeling the
detector simulation, fueled by the advancements in deep generative models.
These models aim to generate responses that are statistically identical to the
observed data. In this paper, we conduct a comprehensive and exhaustive
taxonomic review of the existing literature on the simulation of detector
signatures from both methodological and application-wise perspectives.
Initially, we formulate the problem of detector signature simulation and
discuss its different variations that can be unified. Next, we classify the
state-of-the-art methods into four distinct categories based on their
underlying model architectures, summarizing their respective generation
strategies. We then identify and discuss three key application areas. Finally,
we shed light on the challenges and opportunities that lie ahead in detector
signature simulation, setting the stage for future research and development.

通过以方法论和应用角度综合全面地对探测器特征模拟领域的现有文献进行分类和分析，本文总结了快速模拟、深度生成模型以及探测器特征模拟中的关键应用领域，并对未来的研究和发展提出了挑战和机遇。

深度生成模型用于探测器特征模拟：分析分类

Deep Generative Models for Detector Signature Simulation: An Analytical  Taxonomy

We develop, discuss, and compare several inference techniques to constrain
theory parameters in collider experiments. By harnessing the latent-space
structure of particle physics processes, we extract extra information from the
simulator. This augmented data can be used to train neural networks that
precisely estimate the likelihood ratio. The new methods scale well to many
observables and high-dimensional parameter spaces, do not require any
approximations of the parton shower and detector response, and can be evaluated
in microseconds. Using weak-boson-fusion Higgs production as an example
process, we compare the performance of several techniques. The best results are
found for likelihood ratio estimators trained with extra information about the
score, the gradient of the log likelihood function with respect to the theory
parameters. The score also provides sufficient statistics that contain all the
information needed for inference in the neighborhood of the Standard Model.
These methods enable us to put significantly stronger bounds on effective
dimension-six operators than the traditional approach based on histograms. They
also outperform generic machine learning methods that do not make use of the
particle physics structure, demonstrating their potential to substantially
improve the new physics reach of the LHC legacy results.

利用粒子物理过程的潜在空间结构，利用增强数据训练神经网络，从而实现在加速器实验中约束理论参数的多种推理技术，这些新技术比传统方法基于直方图的方法放置了更强的限制。