On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments

R. Kulkarni, Shruti; Young, Aaron; Date, Prasanna; Rao Miniskar, Narasinga; Vetter, Jeffrey; Fahim, Farah; Parpillon, Benjamin; Dickinson, Jennet; Tran, Nhan; Yoo, Jieun; Mills, Corrinne; Swartz, Morris; Maksimovic, Petar; Schuman, Catherine; Bean, Alice

doi:10.1145/3589737.3605976

Cited by 6 publications

(1 citation statement)

References 26 publications

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“…A recent work (R [215]) implemented an SNN algorithm for filtering data from edge electronics in high energy collider experiments conducted at the High Luminosity Large Hadron Collider (HL-LHC), in order to reduce large data transfer rate or bandwidth (on the order of a few petabytes per second) to downstream electronics. In collider experiments, the collision events of charged particles with energy greater than 2 GeV is of significant interest.…”

Section: Spiking Neural Networkmentioning

confidence: 99%

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: Spiking Neural Networkmentioning

confidence: 99%

Neural network methods for radiation detectors and imaging

Lin,

Ning,

Zhu

et al. 2024

Front. Phys.

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Embedded FPGA developments in 130 nm and 28 nm CMOS for machine learning in particle detector readout

Gonski,

Gupta,

Jia

et al. 2024

J. Inst.

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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 assessed 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.

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Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning

Yoo,

Dickinson,

Swartz

et al. 2024

Mach. Learn.: Sci. Technol.

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Highly granular pixel detectors allow for increasingly precise measurements of charged particle tracks. Next-generation detectors require that pixel sizes will be further reduced, leading to unprecedented data rates exceeding those foreseen at the High-Luminosity Large Hadron Collider. Signal processing that handles data incoming at a rate of O(40 MHz) and intelligently reduces the data within the pixelated region of the detector at rate will enhance physics performance at high luminosity and enable physics analyses that are not currently possible. Using the shape of charge clusters deposited in an array of small pixels, the physical properties of the traversing particle can be extracted with locally customized neural networks. In this first demonstration, we present a neural network that can be embedded into the on-sensor readout and filter out hits from low momentum tracks, reducing the detector’s data volume by 54.4 - 75.4%. The network is designed and simulated as a custom readout integrated circuit with 28 nm CMOS technology and is expected to operate at less than 300 μW with an area of less than 0.2 mm2. The temporal development of charge clusters is investigated to demonstrate possible future performance gains, and there is also a discussion of future algorithmic and technological improvements that could enhance efficiency, data reduction, and power per area.

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On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments

Cited by 6 publications

References 26 publications

Neural network methods for radiation detectors and imaging

Neural network methods for radiation detectors and imaging

Embedded FPGA developments in 130 nm and 28 nm CMOS for machine learning in particle detector readout

Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning

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