Accelerator Module Research Articles

Design and Implementation of Hardware-Software Architecture Based on Hashes for SPHINCS+

Advances in quantum computing have posed a future threat to today’s cryptography. With the advent of these quantum computers, security could be compromised. Therefore, the National Institute of Standards and Technology (NIST) has issued a request for proposals to standardize algorithms for post-quantum cryptography (PQC), which is considered difficult to solve for both classical and quantum computers. Among the proposed technologies, the most popular choices are lattice-based (shortest vector problem) and hash-based approaches. Other important categories are public key cryptography (PKE) and digital signatures. Within the realm of digital signatures lies SPHINCS+. However, there are few implementations of this scheme in hardware architectures. In this article, we present a hardware-software architecture for the SPHINCS+ scheme. We utilized a free RISC-V (Reduced Instruction Set Computer) processor synthesized on a Field Programmable Gate Array (FPGA), primarily integrating two accelerator modules for Keccak-1600 and the Haraka hash function. Additionally, modifications were made to the processor to accommodate the execution of these added modules. Our implementation yielded a 15-fold increase in performance with the SHAKE-256 function and nearly 90-fold improvement when using Haraka, compared to the reference software. Moreover, it is more compact compared to related works. This implementation was realized on a Xilinx FPGA Arty S7: Spartan-7.

Advanced Laser–Plasma Diagnostics for a Modular High-Repetition-Rate Plasma Electron Accelerator

We present a laser–plasma electron accelerator module designed to be driven by high-repetition-rate lasers for industrial applications of laser-driven electron beams. It consists of a single vacuum chamber containing all the necessary components for producing, optimizing, and monitoring electron beams generated via laser wakefield acceleration in a gas jet when driven by a suitable laser. The core methods in this paper involve a comprehensive metrological assessment of the driving laser by rigorous temporal laser pulse characterization and contrast measurements, supplemented by detailed spatiotemporal distribution analyses of the laser focus. Results demonstrate the good stability and reproducibility of the laser system, confirming its suitability for advanced scientific and industrial applications. We further demonstrate the functionality of the laser–plasma accelerator module diagnostics, perform target density characterizations, and time-resolved laser–plasma shadowgraphy. Current limitations of the set-up preventing first electron acceleration are analyzed and an outlook for future experiments is given. Our work is a first step towards the wide dissemination of fully integrated laser–plasma accelerator technology.

Fabrication and testing of the transition section between modules of a wakefield accelerator

The fabrication process is presented for a typical transition section located between each cylindrical corrugated waveguide structure comprising the wakefield accelerator module. The transition section includes couplers for extracting the 180 GHz TM01 accelerating mode and separate couplers for extracting the 190 GHz HE11 dipole mode, both modes induced by the electron bunch traversing the cylindrical corrugated waveguide structure. Extraction of the high-power accelerating mode reduces the heat load due to the subterahertz wave power dissipation within the corrugated accelerating structure. Extraction of the low-power dipole mode serves the purpose of detecting the electron bunch transverse oscillations within the wakefield accelerator and identifying the onset of beam breakup instability. Comprehensive testing of the fully functional transition section with an electron beam was done at the Accelerator Test Facility in Brookhaven National Laboratory which verified the functionality of the transition section. Published by the American Physical Society 2024

A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators

Particle accelerators are complex systems that focus, guide, and accelerate intense charged particle beams to high energy. Beam diagnostics present a challenging problem due to limited non-destructive measurements, computationally demanding simulations, and inherent uncertainties in the system. We propose a two-step unsupervised deep learning framework named as Conditional Latent Autoregressive Recurrent Model (CLARM) for learning the spatiotemporal dynamics of charged particles in accelerators. CLARM consists of a Conditional Variational Autoencoder transforming six-dimensional phase space into a lower-dimensional latent distribution and a Long Short-Term Memory network capturing temporal dynamics in an autoregressive manner. The CLARM can generate projections at various accelerator modules by sampling and decoding the latent space representation. The model also forecasts future states (downstream locations) of charged particles from past states (upstream locations). The results demonstrate that the generative and forecasting ability of the proposed approach is promising when tested against a variety of evaluation metrics.

Interactive simulations as a tool for logistics and maintenance of IFMIF-DONES

Pre-configured virtual reality (VR) simulations of the logistics and maintenance processes have proven to be useful for identifying potential design issues as well as planning operations during an early design phase of facility. But VR simulations can also be used to deeply explore the feasibility of these procedures in a more interactive manner, so that we can identify potential risks and difficulty levels from early stages and study different maintenance strategies to assist the maintenance worker during these procedures. This article presents a framework to design and validate logistics and maintenance procedures in complex facilities, such as the International Fusion Materials Irradiation Facility DEMO Oriented Neutron Source (IFMIF-DONES). Our framework begins with a preparatory phase where essential information about the procedures and Computer-Aided Design (CAD) models is compiled into a comprehensive Virtualization Task Document (VTD). Differently from previous work, this VTD allows representation of parallel tasks. We implement the interactive version of the virtual environment, where the different maintenance and logistics equipment, as well as a virtual maintenance worker is controlled by the user (the person executing the interactive simulation). We have validated this interactive framework with two simulations for the installation process of the Superconducting Radio Frequency Linear accelerator (SRF Linac) modules in IFMIF-DONES. In one simulation (the automatic one), the procedures are reproduced as they are planned, while in the second simulation (the interactive one) the user freely controls the movements of the moving parts of the crane, grab and release plant equipment, move platforms, etc. Based on our simulations, the interactive version allows easier detection of potential points of collisions as well as more precise assessment of the difficulty of the tasks to be performed.

Pattern Recognition in Embedded Systems for Event Occurrences

Abstract: PR (pattern recognition) typically includes interaction with humans and other complicated processes in the real world, embedded systems are ideal candidates. A typical PR application often considered the more perceptual branch of AI, responds to external events that the system detects through physical sensors or input devices by activating actuators or displaying relevant information. To explore the embedded recognition system and apply the deep learning algorithm to face detection, the deep learning-based Convolutional Neural Network (CNN)suggests two deep face detection methods. These are presented to use the deep learning algorithm.This was done to make it possible for us to use the deep learning algorithm for face detection. Because of this, to analyze the built-in face recognition system and applied the deep learning algorithm to the process of identifying faces. In addition, to do both things simultaneously. OMTCNN's training accuracy is 85.14%, higher than the unimproved algorithm. Accuracy of the recognition and calculation acceleration modules boosts embedded system face detection and identification performance. Embedded deep learning recognition is helpful.

Momentum accelerated unfolding network with spectral–spatial prior for computational spectral imaging

Edge computing is a key technology in computational imaging, where the algorithms determine reconstruction quality and reconstruction speed. Recovering spectral information from ill-conditioned data of coded aperture snapshots is a novel but challenging solution in spectral imaging for dynamic scenes. Further study is necessary to enhance the edge computing power of computational spectral programs. Deep unfolding networks have made considerable headway in this direction, but the existing methods still have two drawbacks: (1) The 3D spectral cube exhibits long-range dependency and non-local self-similarity in both spatial and spectral dimensions, yet learn the global features of spatial and spectral is difficult, which remains to be investigated. (2) The global features degenerate as the number of iterations increases. To solve the above problems, this paper proposes a Momentum Accelerated Unfolding Network with Spectral–Spatial prior (MAUNSS). Specifically, a denoising module based on a spectral–spatial transformer is designed, which uses both sparse spectral features to recover global information and dense spatial features to enhance detail texture. Within such a framework, cross-stage feature transmission channels transmitting features across spectral and spatial transformers at different stages are built to avoid feature degeneration. In order to increase the training speed and save the time and cost associated with the iterative approach, we improve it by means of the momentum acceleration module. Supplemented by a re-projection loss, a technique introduced from 3D measurement, the accuracy is further improved. To the best of our knowledge, the proposed method is an attempt to use momentum to expedite the convergence of unfolding networks. Extensive experiments demonstrate that our proposed method outperforms previous state-of-the-art methods by more than 1.94 dB.

Measurement and analysis on distribution of radiation field dose inside the treatment room of TaiChi Pro multimodal integrated digital radiotherapy system

ObjectiveTo examine the leakage radiation dose from the accelerator module's primary beam self-shielding, as well as stray radiation levels and distribution in the treatment room across various planes for the innovative X/γ multimodal radiotherapy system, TaiChi Pro, which is the world's first digitally integrated system that combines a linear accelerator, multi-source rotational focused gamma knife, and multi-modal image guidance, in order to formulate treatment room safety strategies and estimate additional radiation doses to patients. MethodsThe TaiChi Pro multi-mode integrated radiotherapy system was utilized with a 6 ​MV X-ray accelerator in the flatten-filter free (FFF) treatment mode. The system has a primary radiation beam self-shielding, and an isocenter dose rate of 800 ​cGy/min. For detection points in various planes, an x, y, z coordinate system was established with the isocenter as the origin. To simulate the situation of patient, water-equivalent phantom was employed. The ambient dose equivalent and ambient dose equivalent rate at detection points in the radiation field were measured using thermoluminescence dosimeters. ResultsThe self-shielding area achieved a maximum dose of 114.0 ​mGy at 1 m distance from the target point after 3 ​min of cumulative irradiation. The leakage radiation ratios at all points remained below 0.5%, fulfilling the self-shielding design requirements. Regarding the couch, the highest radiation was observed in the opposite direction of the beam at the target point. The stray radiation on the treatment bed plane resulted in an ambient dose equivalent that did not exceed 0.5% of the ambient dose equivalent at the central point. ConclusionThe levels and distribution patterns of stray and leakage radiation inside the TaiChi Pro treatment room meet the requirements of the relevant national standard. The measured values of indoor radiation levels and distribution are very useful for room shielding design and personnel protection.

Research on the construction method of neural network model based on memristors

Abstract With the continuous development of neural networks, the hardware requirements are getting higher and higher, and the emergence of memristors is expected to optimize this challenge. In this paper, by studying the inter-mapping relationship between memristor arrays and neural networks, a memristor-based convolutional neural network circuit module is designed, a binarization method is used to quantize the neural network, an acceleration module is constructed based on multiple computational arrays, and an additive tree is used to accumulate the intermediate results of the output values of multiple computational units. Simulation experiments were conducted with the help of simulation software to compare and analyze the memristor-based CNN with other neural networks. Compared with LSTM, RNN, and ELM, the accuracy of the memristor-based CNN is 4.17%, 7.48%, and 2.01% higher compared to other neural networks, respectively. In the performance analysis, the recognition rate of the memristor CNN is almost unaffected by the programming error and still achieves a recognition rate close to 98% in the best case. This study provides a new idea for implementing and applying convolutional neural networks in memristor arrays, which is expected to promote the further development of memristor neuromorphic computing.

Single-Event Effects on Commercial-Off-the-Shelf Edge-Processing Artificial Intelligence ASICs

Single-event effects test results are presented for heavy ions and high-energy protons on two commercial edge-processing artificial intelligence application-specific integrated circuits, particularly the Google Coral Accelerator Module, which employs a Google Coral Edge Tensor Processing Unit, and the Intel Neural Compute Stick 2, which employs the Intel Movidius Myriad X. The observed SEE signatures, probable causes within the devices, and single-event functional interrupt cross-sections are discussed. Ultimately, when SEUs or SEFIs are observed, there are negligible changes in the measured precision or accuracy of the algorithm, and rarely do the errors result in incorrect identification or classification.

Synthetic Smartwatch IMU Data Generation from In-the-wild ASL Videos

The scarcity of training data available for IMUs in wearables poses a serious challenge for IMU-based American Sign Language (ASL) recognition. In this paper, we ask the following question: can we "translate" the large number of publicly available, in-the-wild ASL videos to their corresponding IMU data? We answer this question by presenting a video to IMU translation framework (Vi2IMU) that takes as input user videos and estimates the IMU acceleration and gyro from the perspective of user's wrist. Vi2IMU consists of two modules, a wrist orientation estimation module that accounts for wrist rotations by carefully incorporating hand joint positions, and an acceleration and gyro prediction module, that leverages the orientation for transformation while capturing the contributions of hand movements and shape to produce realistic wrist acceleration and gyro data. We evaluate Vi2IMU by translating publicly available ASL videos to their corresponding wrist IMU data and train a gesture recognition model purely using the translated data. Our results show that the model using translated data performs reasonably well compared to the same model trained using measured IMU data.

A negative-work knee energy harvester based on homo-phase transfer for wearable monitoring devices

Wearable health monitoring devices can effectively capture human body information and are widely used in health monitoring, but battery life is an important bottleneck in its development. A full negative-work energy harvester based on the homo-phase transfer mechanism by analyzing human motion characteristics was proposed in this paper. The system was designed based on the homo-phase transfer mechanism, including a motion input module, gear acceleration module, energy conversion module, and electric energy storage module. The output performance in three human-level, downhill, and running states was tested, respectively. Finally, we have evaluated the feasibility of an energy harvester powering wearable health monitoring devices, and the harvester can generate 17.40 J/day power, which can satisfy the normal operation of a typical health monitoring device. This study has certain promoting significance for the development of a new generation of human health monitoring.

Design of a nonvolatile-register-embedded RISC-V CPU with software-controlled data-retention and hardware-acceleration functions

This paper describes the design of a nonvolatile CPU based on RISC-V that is an open-source and highly flexible instruction set architecture. This CPU incorporates nonvolatile registers utilizing magnetic tunnel junction (MTJ) device, as well as custom instructions specific to the control of these nonvolatile registers and an accelerator module embedded into the CPU architecture. These techniques enable efficient execution of intermittent operations suitable for energy-limited internet-of-things (IoT) applications. Through performance evaluation of the CPU designed in a 55-nm CMOS/MTJ-hybrid process technology, we show that our CPU can save up to 56.9% of power consumption compared to conventional ones, with an average power consumption of 3.91 μW/MHz.

Categorical Embeddings for Tabular Data using PyTorch

Deep learning has received much attention for computer vision and natural language processing, but less for tabular data, which is the most prevalent type of data used in industry. Embeddings offer a solution by representing categorical variables as continuous vectors in lowdimensional space. PyTorch provides excellent support for GPU acceleration and pre-built functions and modules, making it easier to work with embeddings and categorical variables. In this research paper, we apply a feedforward neural network model in PyTorch to a multiclass classification problem using the Shelter Animal Outcome dataset. We calculate the probability of an animal's outcome belonging to each of the 5 categories. Additionally, we explore feature importance using two common techniques: MDI and permutation. Understanding feature importance is crucial for building better models, improving performance, and interpreting and communicating results. Our findings demonstrate the usefulness of embeddings and PyTorch for deep learning with tabular data and highlight the importance of feature selection for building effective machine learning models.

Energy Saving Multi-Relay Technique for Wireless Sensor Networks Based on Hw/Sw MPSoC System

Currently, we are witnessing a wide range of Wireless Sensor Networks (WSNs) in various fields of application. In most cases, these networks contain numerous battery-powered sensor nodes. Therefore, the lifetime of WSNs is intrinsically dependent. For this purpose, different studies have been conducted to reduce the power consumption. We propose a new technique for reducing the power consumption of clustered WSNs. We considered clusters in which sensors transmitted data to a Cluster Head (CH) by cooperative communication based on the Bit Error Probability (BEP) criterion for the multi-relay technique. Therefore, the proposed technique uses a multi-relay to reduce the transmission power to communicate between the source and destination which respects the predefined reliability level. The proof of the described technique was performed using a multiprocessor architecture. Second, we used an efficient software/hardware (Sw/Hw) architecture that utilized accelerator modules. The experimental results demonstrate significant gains in terms of energy consumption and workload compared to the SW implementation.

Dendrite Net with Acceleration Module for Faster Nonlinear Mapping and System Identification

Nonlinear mapping is an essential and common demand in online systems, such as sensor systems and mobile phones. Accelerating nonlinear mapping will directly speed up online systems. Previously the authors of this paper proposed a Dendrite Net (DD) with enormously lower time complexity than the existing nonlinear mapping algorithms; however, there still are redundant calculations in DD. This paper presents a DD with an acceleration module (AC) to accelerate nonlinear mapping further. We conduct three experiments to verify whether DD with AC has lower time complexity while retaining DD’s nonlinear mapping properties and system identification properties: The first experiment is the precision and identification of unary nonlinear mapping, reflecting the calculation performance using DD with AC for basic functions in online systems. The second experiment is the mapping precision and identification of the multi-input nonlinear system, reflecting the performance for designing online systems via DD with AC. Finally, this paper compares the time complexity of DD and DD with AC and analyzes the theoretical reasons through repeated experiments. Results: DD with AC retains DD’s excellent mapping and identification properties and has lower time complexity. Significance: DD with AC can be used for most engineering systems, such as sensor systems, and will speed up computation in these online systems.

Meter-scale plasma waveguides for multi-GeV laser wakefield acceleration

We present results from two new techniques for the generation of meter-scale, low density (∼1017 cm−3 on axis) plasma waveguides, the “two-Bessel” technique, and the “self-waveguiding” technique. Plasma waveguides of this density and length range are needed for demonstration of a ∼10 GeV laser wakefield accelerator module, key for future staging for a ∼TeV lepton collider. Both techniques require the use of high quality ultrashort pulse Bessel beams to efficiently and uniformly ionize hydrogen gas in meter-scale supersonic gas jets via optical field ionization. We review these two techniques, describe our meter-scale gas jets, and present a new method for correction of optical aberrations in Bessel beams. Finally, we briefly present results from recent experiments employing one of our techniques, demonstrating quasi-monoenergetic acceleration of ∼5 GeV electron bunches in 20 cm long, low density plasma waveguides.

Conceptual design of DALS test facility cryogenic system

Dalian advanced light source (DALS) is a new project proposed by Dalian Institute of Chemical Physics (DICP) aiming to produce high-quality electron beam with repetition rate up to 100 kHz and the project is subject to central government approval. The cryomodules and superconducting cavities need to be tested before assembled in the real accelerator so the test facility is necessary. The Accelerator module test facility (AMTF) project has been funded by Dalian local government and is currently under construction as the pre-research project for DALS, it consists of four testbenches, one Horizontal testbench (HTB) for cryomodule test, one Vertical testbench (VTB) for cavity test, one Cryogenic testbench (CTB) for cryogenic test and one Injector testbench (ITB) for beam test. A Test facility cryoplant (TFCP) with capacity of 370 W@2 K will be used for cooling testbenches. This paper presents the design and current progress of the TFCP system, which includes the refrigerators, helium compressors, process vacuum pump system (PVPS), gas storage system, recovery and purification system.

Investigation of Transverse Instability of a High-Current Relativistic Electron Beam in a Linear Induction Accelerator

The article presents the results of studies on the transverse instability of a high-current relativistic electron beam developing in a linear induction accelerator LIA for 5 MeV electron energy, which is created at the BINP SB RAS together with RFNC VNIITF. These results were obtained using a software package that makes it possible to simulate the dynamics of the instability development, as well as to calculate the increment of this instability averaged over the accelerator length. The package consists of four main parts. The first of them, made on the base of a three-dimensional model of the accelerating module electrodynamic system of the LIA, allows calculating the main characteristics of electromagnetic dipole modes of such a module, the second and third parts are designed to find three-dimensional accelerating electric and focusing magnetic fields, respectively. In the last part of the package, a system of ordinary differential equations is solved that describes both the motion of beam macroparticles in electric and magnetic fields, including the eigenmode fields, and the excitation of the mode fields by the electron beam. The adequacy of the physical models used in the software package was tested by comparing the spectra of field oscillations in the accelerator modules obtained in calculations and recorded in the experiment. On the base of the data obtained, the main regularities of the transverse beam instability development in the frequency range ∆f = 0.3–1.1 GHz were revealed, and possible methods for suppressing this instability in the LIA were proposed.

Low cost multifunctional 3D printed image quality and dose verification phantom for an image-guided radiotherapy system.

PurposeImage-guided radiation therapy (IGRT) is used to precisely deliver radiation to a tumour to reduce the possible damage to the surrounding normal tissues. Clinics use various quality assurance (QA) equipment to ensure that the performance of the IGRT system meets the international standards set for the system. The objective of this study was to develop a low-cost and multipurpose module for evaluating image quality and dose.MethodsA multipurpose phantom was designed to meet the clinical requirements of high accuracy, easy setup, and calibration. The outer shell of the phantom was fabricated using acrylic. Three dimensional (3D) printing technology was used to fabricate inner slabs with the characteristics of high spatial resolution, low-contrast detectability, a 3D grid, and liquid-filled uniformity. All materials were compatible with magnetic resonance (MR). Computed tomography (CT) simulator and linear accelerator (LINAC) modules were developed and validated.ResultsThe uniformity slab filled with water is ideal for the assessment of Hounsfield units, whereas that filled with wax is suitable for consistency checks. The high-spatial-resolution slab enables measurements with a resolution up to 5 lp/cm. The low-contrast detectability slab contains rods of 5 different sizes that can be clearly visualised. These components meet the American College of Radiology (ACR) standards for QA of CT simulators and LINACs.ConclusionsThe multifunctional phantom module meets the ACR recommended QA guidelines and is suitable for both LINACs and CT-sim. Further measurements in an MR simulator and an MR linear accelerator (MR-LINAC) will be arranged in the future.