Privacy is What We Care About: Experimental Investigation of Federated Learning on Edge Devices

Federated Learning enables training of a general model through edge devices without sending raw data to the cloud. Hence, this approach is attractive for digital health applications, where data is sourced through edge devices and users care about privacy. Here, we report on the feasibility to train deep neural networks on the Raspberry Pi4s as edge devices. A CNN, a LSTM and a MLP were successfully trained on the MNIST data-set. Further, federated learning is demonstrated experimentally on IID and non-IID samples in a parametric study, to benchmark the model convergence. The weight updates from the workers are shared with the cloud to train the general model through federated learning. With the CNN and the non-IID samples a test-accuracy of up to 85% could be achieved within a training time of 2 minutes, while exchanging less than $10$ MB data per device. In addition, we discuss federated learning from an use-case standpoint, elaborating on privacy risks and labeling requirements for the application of emotion detection from sound. Based on the experimental findings, we discuss possible research directions to improve model and system performance. Finally, we provide best practices for a practitioner, considering the implementation of federated learning.Comment: Accepted in ACM AIChallengeIoT 2019, New York, US

AB2CD: AI for Building Climate Damage Classification and Detection

We explore the implementation of deep learning techniques for precise building damage assessment in the context of natural hazards, utilizing remote sensing data. The xBD dataset, comprising diverse disaster events from across the globe, serves as the primary focus, facilitating the evaluation of deep learning models. We tackle the challenges of generalization to novel disasters and regions while accounting for the influence of low-quality and noisy labels inherent in natural hazard data. Furthermore, our investigation quantitatively establishes that the minimum satellite imagery resolution essential for effective building damage detection is 3 meters and below 1 meter for classification using symmetric and asymmetric resolution perturbation analyses. To achieve robust and accurate evaluations of building damage detection and classification, we evaluated different deep learning models with residual, squeeze and excitation, and dual path network backbones, as well as ensemble techniques. Overall, the U-Net Siamese network ensemble with F-1 score of 0.812 performed the best against the xView2 challenge benchmark. Additionally, we evaluate a Universal model trained on all hazards against a flood expert model and investigate generalization gaps across events, and out of distribution from field data in the Ahr Valley. Our research findings showcase the potential and limitations of advanced AI solutions in enhancing the impact assessment of climate change-induced extreme weather events, such as floods and hurricanes. These insights have implications for disaster impact assessment in the face of escalating climate challenges.Comment: 9 pages, 4 figure

Experimental investigation into vortex structure and pressure drop across microcavities in 3D integrated electronics

Hydrodynamics in microcavities with cylindrical micropin fin arrays simulating a single layer of a water-cooled electronic chip stack is investigated experimentally. Both inline and staggered pin arrangements are investigated using pressure drop and microparticle image velocimetry (μPIV) measurements. The pressure drop across the cavity shows a flow transition at pin diameter-based Reynolds numbers (Re d) ~200. Instantaneous μPIV, performed using a pH-controlled high seeding density of tracer microspheres, helps visualize vortex structure unreported till date in microscale geometries. The post-transition flow field shows vortex shedding and flow impingement onto the pins explaining the pressure drop increase. The flow fluctuations start at the chip outlet and shift upstream with increasing Re d. No fluctuations are observed for a cavity with pin height-to-diameter ratio h/d=1 up to Re d ~330; however, its pressure drop was higher than for a cavity with h/d=2 due to pronounced influence of cavity wall

Toward Foundation Models for Earth Monitoring: Generalizable Deep Learning Models for Natural Hazard Segmentation

Climate change results in an increased probability of extreme weather events that put societies and businesses at risk on a global scale. Therefore, near real-time mapping of natural hazards is an emerging priority for the support of natural disaster relief, risk management, and informing governmental policy decisions. Recent methods to achieve near real-time mapping increasingly leverage deep learning (DL). However, DL-based approaches are designed for one specific task in a single geographic region based on specific frequency bands of satellite data. Therefore, DL models used to map specific natural hazards struggle with their generalization to other types of natural hazards in unseen regions. In this work, we propose a methodology to significantly improve the generalizability of DL natural hazards mappers based on pre-training on a suitable pre-task. Without access to any data from the target domain, we demonstrate this improved generalizability across four U-Net architectures for the segmentation of unseen natural hazards. Importantly, our method is invariant to geographic differences and differences in the type of frequency bands of satellite data. By leveraging characteristics of unlabeled images from the target domain that are publicly available, our approach is able to further improve the generalization behavior without fine-tuning. Thereby, our approach supports the development of foundation models for earth monitoring with the objective of directly segmenting unseen natural hazards across novel geographic regions given different sources of satellite imagery.Comment: Accepted at IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2023

3D-ICE: a Compact Thermal Model for Early-Stage Design of Liquid-Cooled ICs

Liquid-cooling using microchannel heat sinks etched on silicon dies is seen as a promising solution to the rising heat fluxes in two-dimensional and stacked three-dimensional integrated circuits. Development of such devices requires accurate and fast thermal simulators suitable for early-stage design. To this end, we present 3D-ICE, a compact transient thermal model (CTTM), for liquid-cooled ICs. 3D-ICE was first advanced by incorporating the 4-resistor model based CTTM (4RM-based CTTM). It was enhanced to speed up simulations and to include complex heat sink geometries such as pin fins using the new 2 resistor model (2RM-based CTTM). In this paper, we extend the 3D-ICE model to include liquid-cooled ICs with multi-port cavities, i.e., cavities with more than one inlet and one outlet ports, and non-straight microchannels. Simulation studies using a realistic 3D multiprocessor system-on-chip (MPSoC) with a 4-port microchannel cavity highlight the impact of using 4-port cavity on temperature and also demonstrate the superior performance of 2RM-based CTTM compared to 4RM-based CTTM. We also present an extensive review of existing literature and the derivation of the 3D-ICE model, creating a comprehensive study of liquid-cooled ICs and their thermal simulation from the perspective of computer systems design. Finally, the accuracy of 3D-ICE has been evaluated against measurements from a real liquid-cooled 3D IC, which is the first such validation of a simulator of this genre. Results show strong agreement (average error<10%), demonstrating that 3D-ICE is an effective tool for early-stage thermal-aware design of liquid-cooled 2D/3D ICs

Multisensory Home-Monitoring in Individuals With Stable Chronic Obstructive Pulmonary Disease and Asthma: Usability Study of the CAir-Desk

Background: Research integrating multisensory home-monitoring in respiratory disease is scarce. Therefore, we created a novel multisensory home-monitoring device tailored for long-term respiratory disease management (named the CAir-Desk). We hypothesize that recent technological accomplishments can be integrated into a multisensory participant-driven platform. We also believe that this platform could improve chronic disease management and be accessible to large groups at an acceptable cost. Objective: This study aimed to report on user adherence and acceptance as well as system functionality of the CAir-Desk in a sample of participants with stable chronic obstructive pulmonary disease (COPD) or asthma. Methods: We conducted an observational usability study. Participants took part in 4 weeks of home-monitoring with the CAir-Desk. The CAir-Desk recorded data from all participants on symptom burden, physical activity, spirometry, and environmental air quality; data on sputum production, and nocturnal cough were only recorded for participants who experienced symptoms. After the study period, participants reported on their perceptions of the usability of the monitoring device through a purpose-designed questionnaire. We used descriptive statistics and visualizations to display results. Results: Ten participants, 5 with COPD and 5 with asthma took part in this study. They completed symptom burden questionnaires on a median of 96% (25th percentile 14%, 75th percentile 96%), spirometry recordings on 55% (20%, 94%), wrist-worn physical activity recordings on 100% (97%, 100%), arm-worn physical activity recordings on 45% (13%, 63%), nocturnal cough recordings on 34% (9%, 54%), sputum recordings on 5% (3%, 12%), and environmental air quality recordings on 100% (99%, 100%) of the study days. The participants indicated that the measurements consumed a median of 13 (10, 15) min daily, and that they preferred the wrist-worn physical activity monitor to the arm-worn physical activity monitor. Conclusions: The CAir-Desk showed favorable technical performance and was well-accepted by our sample of participants with stable COPD and asthma. The obtained insights were used in a redesign of the CAir-Desk, which is currently applied in a randomized controlled trial including an interventional program

Vortex shedding from confined micropin arrays

The hydrodynamics in microcavities populated with cylindrical micropins was investigated using dynamic pressure measurements and fluid pathline visualization. Pressure signals were Fourier-analyzed to extract the flow fluctuation frequencies, which were in the kHz range for the tested flow Reynolds numbers (Re) of up to 435. Three different sets of flow dependent characteristic frequencies were identified, the first due to vortex shedding, the second due to lateral flow oscillation and the third due to a transition between these two flow regimes. These frequencies were measured at different locations along the chip (e.g. inlet, middle and outlet). It is established that vortex shedding initiates at the outlet and then travels upstream with increase in Re. The pathline visualization technique provided direct optical access to the flow field without any intermediate post-processing step and could be used to interpret the frequencies determined through pressure measurements. Microcavities with different micropin height-to-diameter aspect ratios and pitch-to-diameter ratios were tested. The tests confirmed an increase in the Strouhal number (associated with the vortex shedding) with increased confinement (decrease in the aspect ratio or the pitch), in agreement with macroscale measurements. The compact nature of the microscale geometry tested, and the measurement technique demonstrated, readily enabled us to investigate the flow past 4,420 pins with various degrees of confinements; this makes the measurements performed and the techniques developed here an important tool for investigating large arrays of similar objects in a flow fiel

Machine Learning Techniques for Personalized Detection of Epileptic Events in Clinical Video Recordings

Continuous patient monitoring is essential to achieve an effective and optimal patient treatment in the intensive care unit. In the specific case of epilepsy it is the only way to achieve a correct diagnosis and a subsequent optimal medication plan if possible. In addition to automatic vital sign monitoring, epilepsy patients need manual monitoring by trained personnel, a task that is very difficult to be performed continuously for each patient. Moreover, epileptic manifestations are highly personalized even within the same type of epilepsy. In this work we assess two machine learning methods, dictionary learning and an autoencoder based on long short-term memory (LSTM) cells, on the task of personalized epileptic event detection in videos, with a set of features that were specifically developed with an emphasis on high motion sensitivity. According to the strengths of each method we have selected different types of epilepsy, one with convulsive behaviour and one with very subtle motion. The results on five clinical patients show a highly promising ability of both methods to detect the epileptic events as anomalies deviating from the stable/normal patient status

Integration of Intra Chip Stack Fluidic Cooling using Thin-Layer Solder Bonding

Three-dimensional (3D) stacking of integrated circuit (IC) dies by vertical integration increases system density and package functionality. The vertical integration of IC dies by area-array Through-Silicon-Vias (TSVs) reduces the length of global interconnects and accordingly the signal delay time. On the other hand, the ongoing miniaturization trend of ICs results in constantly increasing chip-level power densities. Thus, the development of new chip cooling concepts is of utmost importance. Therefore, scalable cooling solutions for chip stacks, such as interlayer cooling, need to be investigated. This paper presents a new concept for the integration of intra chip stack fluidic cooling, namely die-embedded microchannels for single- and twophase thermal management, using a patterned thin-layer eutectic solder bonding technique for the stack assembly. Results showed the successful fabrication of 5-layer chip stacks with embedded microchannels and high aspect ratio TSVs. Optical inspections demonstrated the proper bond line formation and direct current (DC) daisy-chain electrical tests indicated the successful combination of TSVs with thin-layer solder interconnects. Mechanical shear tests on die-on-die bonded samples showed the strength of the patterned thin-layer solder bond (16MPa). An added solder ring-pad component to seal the electrically active pad from any conductive liquid coolant was also investigated and reflow tests on such geometries showed the appearance of a balling effect along the solder ring line. This balling was found to be mitigated when the ring aspect ratio (deposited solder height to ring width ratio) was kept below the experimentally observed critical value of 0.65

Towards Thermally-Aware Design of 3D MPSoCs with Inter-Tier Cooling

Abstract—New tendencies envisage 3D Multi-Processor System-On-Chip (MPSoC) design as a promising solution to keep increasing the performance of the next-generation highperformance computing (HPC) systems. However, as the power density of HPC systems increases with the arrival of 3D MPSoCs, supplying electrical power to the computing equipment and constantly removing the generated heat is rapidly becoming the dominant cost in any HPC facility. Thus, both power and thermal/cooling implications play a major role in the design of new HPC systems, given the energy constraints in our society. Therefore, EPFL, IBM and ETHZ have been working within the CMOSAIC Nano-Tera.ch program project in the last three years on the development of a holistic thermally-aware design. This paper presents the exploration in CMOSAIC of novel cooling technologies, as well as suitable thermal modeling and system-level design methods, which are all necessary to develop 3D MPSoCs with inter-tier liquid cooling systems. As a result, we develop energy-efficient run-time thermal control strategies to achieve energy-efficient cooling mechanisms to compress almost 1 Tera nano sized functional units into one cubic centimeter with a 10 to 100 fold higher connectivity than otherwise possible. The proposed thermally-aware design paradigm includes exploring the synergies of hardware-, software- and mechanical-based thermal control techniques as a fundamental step to design 3D MPSoCs for HPC systems. More precisely, we target the use of inter-tier coolants ranging from liquid water and twophase refrigerants to novel engineered environmentally friendly nano-fluids, as well as using specifically designed micro-channel arrangements, in combination with the use of dynamic thermal management at system-level to tune the flow rate of the coolant in each micro-channel to achieve thermally-balanced 3D-ICs. Our management strategy prevents the system from surpassing the given threshold temperature while achieving up to 67% reduction in cooling energy and up to 30% reduction in system-level energy in comparison to setting the flow rate at the maximum value to handle the worst-case temperature