29 research outputs found
Summary Report Topical Group on Application and Industry Community Engagement Frontier Snowmass 2021
HEP community leads and operates cutting-edge experiments for the DOE Office
of Science which have challenging sensing, data processing, and computing
requirements that far surpass typical industrial applications. To make
necessary progress in the energy, material, and fundamental sciences,
development of novel technologies is often required to enable these advanced
detector and accelerator programs. Our capabilities include efficient
co-design, which is a prerequisite to enable the deployment of advanced
techniques in a scientific setting where development spans from rapid
prototyping to robust and reliable production scale. This applies across the
design spectrum from the low level fabrication techniques to the high level
software development. It underpins the requirement for a holistic approach of
innovation that accelerates the cycle of technology development and deployment.
The challenges set by the next generation of experiments requires a
collaborative approach between academia, industry and national labs. Just a
single stakeholder will be unable to deliver the technologies required for the
success of the scientific goals. Tools and techniques developed for High Energy
Physics (HEP) research can accelerate scientific discovery more broadly across
DOE Office of Science and other federal initiatives and also benefit industry
applications
Optimizing floating guard ring designs for FASPAX N-in-P silicon sensors
FASPAX (Fermi-Argonne Semiconducting Pixel Array X-ray detector) is being
developed as a fast integrating area detector with wide dynamic range for time
resolved applications at the upgraded Advanced Photon Source (APS.) A burst
mode detector with intended \mbox{13 MHz} image rate, FASPAX will also
incorporate a novel integration circuit to achieve wide dynamic range, from
single photon sensitivity to x-rays/pixel/pulse. To achieve
these ambitious goals, a novel silicon sensor design is required. This paper
will detail early design of the FASPAX sensor. Results from TCAD optimization
studies, and characterization of prototype sensors will be presented.Comment: IEEE NSS-MIC 2015 Conference recor
Memory-Immersed Collaborative Digitization for Area-Efficient Compute-in-Memory Deep Learning
This work discusses memory-immersed collaborative digitization among
compute-in-memory (CiM) arrays to minimize the area overheads of a conventional
analog-to-digital converter (ADC) for deep learning inference. Thereby, using
the proposed scheme, significantly more CiM arrays can be accommodated within
limited footprint designs to improve parallelism and minimize external memory
accesses. Under the digitization scheme, CiM arrays exploit their parasitic bit
lines to form a within-memory capacitive digital-to-analog converter (DAC) that
facilitates area-efficient successive approximation (SA) digitization. CiM
arrays collaborate where a proximal array digitizes the analog-domain
product-sums when an array computes the scalar product of input and weights. We
discuss various networking configurations among CiM arrays where Flash, SA, and
their hybrid digitization steps can be efficiently implemented using the
proposed memory-immersed scheme. The results are demonstrated using a 65 nm
CMOS test chip. Compared to a 40 nm-node 5-bit SAR ADC, our 65 nm design
requires 25 less area and 1.4 less energy by
leveraging in-memory computing structures. Compared to a 40 nm-node 5-bit Flash
ADC, our design requires 51 less area and 13 less
energy
Neural network accelerator for quantum control
Efficient quantum control is necessary for practical quantum computing
implementations with current technologies. Conventional algorithms for
determining optimal control parameters are computationally expensive, largely
excluding them from use outside of the simulation. Existing hardware solutions
structured as lookup tables are imprecise and costly. By designing a machine
learning model to approximate the results of traditional tools, a more
efficient method can be produced. Such a model can then be synthesized into a
hardware accelerator for use in quantum systems. In this study, we demonstrate
a machine learning algorithm for predicting optimal pulse parameters. This
algorithm is lightweight enough to fit on a low-resource FPGA and perform
inference with a latency of 175 ns and pipeline interval of 5 ns with 0.99
gate fidelity. In the long term, such an accelerator could be used near quantum
computing hardware where traditional computers cannot operate, enabling quantum
control at a reasonable cost at low latencies without incurring large data
bandwidths outside of the cryogenic environment.Comment: 7 pages, 10 figure
A Sub-Electron-Noise Multi-Channel Cryogenic Skipper-CCD Readout ASIC
The \emph{MIDNA} application specific integrated circuit (ASIC) is a
skipper-CCD readout chip fabricated in a 65 nm LP-CMOS process that is capable
of working at cryogenic temperatures. The chip integrates four front-end
channels that process the skipper-CCD signal and performs differential
averaging using a dual slope integration (DSI) circuit. Each readout channel
contains a pre-amplifier, a DC restorer, and a dual-slope integrator with
chopping capability. The integrator chopping is a key system design element in
order to mitigate the effect of low-frequency noise produced by the integrator
itself, and it is not often required with standard CCDs. Each channel consumes
4.5 mW of power, occupies 0.156 mm area and has an input referred noise
of 2.7. It is demonstrated experimentally to achieve
sub-electron noise when coupled with a skipper-CCD by means of averaging
samples of each pixel. Sub-electron noise is shown in three different
acquisition approaches. The signal range is 6000 electrons. The readout system
achieves 0.2 RMS by averaging 1000 samples with MIDNA both at room
temperature and at 180 Kelvin
On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments
This work describes the investigation of neuromorphic computing-based spiking
neural network (SNN) models used to filter data from sensor electronics in high
energy physics experiments conducted at the High Luminosity Large Hadron
Collider. We present our approach for developing a compact neuromorphic model
that filters out the sensor data based on the particle's transverse momentum
with the goal of reducing the amount of data being sent to the downstream
electronics. The incoming charge waveforms are converted to streams of
binary-valued events, which are then processed by the SNN. We present our
insights on the various system design choices - from data encoding to optimal
hyperparameters of the training algorithm - for an accurate and compact SNN
optimized for hardware deployment. Our results show that an SNN trained with an
evolutionary algorithm and an optimized set of hyperparameters obtains a signal
efficiency of about 91% with nearly half as many parameters as a deep neural
network.Comment: Manuscript accepted at ICONS'2
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices
Accessible machine learning algorithms, software, and diagnostic tools for
energy-efficient devices and systems are extremely valuable across a broad
range of application domains. In scientific domains, real-time near-sensor
processing can drastically improve experimental design and accelerate
scientific discoveries. To support domain scientists, we have developed hls4ml,
an open-source software-hardware codesign workflow to interpret and translate
machine learning algorithms for implementation with both FPGA and ASIC
technologies. We expand on previous hls4ml work by extending capabilities and
techniques towards low-power implementations and increased usability: new
Python APIs, quantization-aware pruning, end-to-end FPGA workflows, long
pipeline kernels for low power, and new device backends include an ASIC
workflow. Taken together, these and continued efforts in hls4ml will arm a new
generation of domain scientists with accessible, efficient, and powerful tools
for machine-learning-accelerated discovery.Comment: 10 pages, 8 figures, TinyML Research Symposium 202
Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis
BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London
Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study
Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world.
Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231.
Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001).
Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication