Systems Simulation Using ProModel: An Online Teaching Method

This study was designed with the purpose to create an online method to deploy the concepts and practical knowledge necessary for the course System Simulation {TEC 5523) offered by Eastern Illinois University. The course is currently offered on a hybrid format, with part of it being ministered in the classroom and the remaining on the online environment. This research focused on creating a set of multimedia and step-by-step lessons aimed to substitute the part of the course that demands the students to be present in the classroom. These lessons were developed based on exercises from the textbook currently used for this class, and were selected in order to guarantee that the key concepts of this course were transmitted to the students. The lessons that resulted from this research were then tested and evaluated by a few students that did not have previous contact with the systems simulation topic. These students were enrolled in the Master of Science program at Eastern Illinois University. The results of this evaluation show that the multimedia lessons and step-by-step guides that resulted from this research are successful in guiding the students in the execution of the lessons that were selected as part of the material

Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors

Although the quest for more accurate solutions is pushing deep learning research towards larger and more complex algorithms, edge devices demand efficient inference and therefore reduction in model size, latency and energy consumption. One technique to limit model size is quantization, which implies using fewer bits to represent weights and biases. Such an approach usually results in a decline in performance. Here, we introduce a method for designing optimally heterogeneously quantized versions of deep neural network models for minimum-energy, high-accuracy, nanosecond inference and fully automated deployment on chip. With a per-layer, per-parameter type automatic quantization procedure, sampling from a wide range of quantizers, model energy consumption and size are minimized while high accuracy is maintained. This is crucial for the event selection procedure in proton-proton collisions at the CERN Large Hadron Collider, where resources are strictly limited and a latency of ${\mathcal O}(1)~\mu$s is required. Nanosecond inference and a resource consumption reduced by a factor of 50 when implemented on field-programmable gate array hardware are achieved

Fast convolutional neural networks on FPGAs with hls4ml

We introduce an automated tool for deploying ultra low-latency, low-power deep neural networks with convolutional layers on FPGAs. By extending the hls4ml library, we demonstrate an inference latency of $5\,\mu$s using convolutional architectures, targeting microsecond latency applications like those at the CERN Large Hadron Collider. Considering benchmark models trained on the Street View House Numbers Dataset, we demonstrate various methods for model compression in order to fit the computational constraints of a typical FPGA device used in trigger and data acquisition systems of particle detectors. In particular, we discuss pruning and quantization-aware training, and demonstrate how resource utilization can be significantly reduced with little to no loss in model accuracy. We show that the FPGA critical resource consumption can be reduced by 97% with zero loss in model accuracy, and by 99% when tolerating a 6% accuracy degradation.Comment: 18 pages, 18 figures, 4 table

Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs

We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks. The two complementary FPGA designs are based on OpenCL, a framework for writing programs that execute across heterogeneous platforms, and hls4ml, a high-level-synthesis-based compiler for neural network to firmware conversion. We evaluate and compare the resource usage, latency, and tracking performance of our implementations based on a benchmark dataset. We find a considerable speedup over CPU-based execution is possible, potentially enabling such algorithms to be used effectively in future computing workflows and the FPGA-based Level-1 trigger at the CERN Large Hadron Collider.Comment: 8 pages, 4 figures, To appear in Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020

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

Psychometric assessment of the Internet Gaming Disorder diagnostic criteria: an item response theory study

Internet Gaming Disorder (IGD) has been recognized by the American Psychiatric Association (APA) as a tentative disorder in the latest fifth revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). In order to advance research on IGD, the APA has suggested that further research on the nine IGD criteria to investigate its clinical and empirical feasibility is necessary. The aim of the present study was to develop the Polish the Internet Gaming Disorder Scale–Short-Form (IGDS9-SF) and scrutinize the nine IGD criteria empirically. To achieve this, the newly developed IGDS9-SF was examined using a wide range of psychometric methods, including a polytomous Item Response Theory (IRT) analysis to evaluate the measurement performance of the nine IGD criteria. A sample of 3377 gamers (82.7% male, mean age 20 years, SD = 4.3 years) was recruited online for the present study. Overall, the findings obtained confirmed that suitability of the Polish IGDS9-SF to assess IGD amongst Polish gamers given the adequate levels of validity and reliability found. The IRT analysis revealed that the IGDS9-SF is a suitable tool to measure IGD levels above the average; however, criteria "continuation" (item 6), "deception" (item 7), and "escape" (item 8) presented with poor fit. Taken together, these results suggest that some of the diagnostic criteria may present with a different clinical weighting towards final diagnosis of IGD. The implications of these findings are further discussed

Investigating the differential effects of social networking site addiction and Internet gaming disorder on psychological health

Background and aims: Previous studies focused on examining the interrelationships between social networking site (SNS) addiction and Internet gaming disorder (IGD) in isolation. Moreover, little is known about the potential simultaneous differential effects of SNS addiction and IGD on psychological health. This study investigated the interplay between these two technological addictions and ascertained how they can uniquely and distinctively contribute to increasing psychiatric distress when accounting for potential effects stemming from sociodemographic and technology-related variables. Methods: A sample of 509 adolescents (53.5% males) aged 10–18 years (mean = 13.02, SD = 1.64) were recruited. Results: It was found that key demographic variables can play a distinct role in explaining SNS addiction and IGD. Furthermore, it was found that SNS addiction and IGD can augment the symptoms of each other, and simultaneously contribute to deterioration of overall psychological health in a similar fashion, further highlighting potentially common etiological and clinical course between these two phenomena. Finally, the detrimental effects of IGD on psychological health were found to be slightly more pronounced than those produced by SNS addiction, a finding that warrants additional scientific scrutiny. Discussion and conclusion: The implications of these results are further discussed in light of the existing evidence and debates regarding the status of technological addictions as primary and secondary disorders

A preliminary cross-cultural study of Hikikomori and Internet Gaming Disorder: the moderating effects of game-playing time and living with parents

Background: Internet Gaming Disorder (IGD) and Hikikomori (an extreme form of real-life social withdrawal where individuals isolate themselves from society) have both been suggested as mental disorders that require further clinical research, particularly amongst young adult populations. Objective: To add to the extant literature, the present study used a cross-cultural, cross-sectional design to investigate the association between Hikikomori and IGD, and the potential moderating effects of reported game-playing time and living with parents. Method: Two online samples of 153 Australian and 457 U.S.-North American young adult players of Massively Multiplayer Online (MMO) games were collected. The nine-item Internet Gaming Disorder Scale-Short Form (IGDS-SF9), and the Hikikomori Social Withdrawal Scale were administered to dimensionally assess IGD and Hikikomori, respectively. Results: Linear regression analyses confirmed that Hikikomori symptoms are associated with IGD. Additionally, moderation analyses indicated that the association was exacerbated by longer game playing time across both populations. Gamers living with their parents was a significant moderator of the relationship for the Australian sample. Conclusions: Extreme real-life social withdrawal and IGD are related, and this association is exacerbated for those who spend more time playing MMOs per day, and, for Australian participants, living with their parents