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Automatic Energy-Hotspot Detection and Elimination in Real-Time Deeply Embedded Systems
(2021)2021 IEEE Real-Time Systems Symposium (RTSS)Today’s deeply embedded systems, with real-time interactions to the environment, are largely battery-operated, and peripheral modules like LTE, WiFi, and GPS are among the most energy-hungry components of them. These components are often under the direct control of an embedded software. Some pieces of the software program are called energy hotspots if they can be transformed towards better system energy consumption while leaving it ...Conference Paper -
TIP-Air: Tracking Pollution Transfer for Accurate Air Quality Prediction
(2021)Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers (UbiComp-ISWC '21 Adjunct)Air quality is of vital importance to human health. Accurately predicting air quality, especially its sudden changes, is highly valuable for citizens and governments to make personal and local decisions, design intelligent policies and control pollution at minimal cost. However, none of the existing methods achieves sufficient prediction accuracy for time intervals of sudden pollution change due to inability of existing models to take ...Conference Paper -
Pruning-Aware Merging for Efficient Multitask Inference
(2021)Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD '21)Many mobile applications demand selective execution of multiple correlated deep learning inference tasks on resource-constrained platforms. Given a set of deep neural networks, each pre-trained for a single task, it is desired that executing arbitrary combinations of tasks yields minimal computation cost. Pruning each network separately yields suboptimal computation cost due to task relatedness. A promising remedy is to merge the networks ...Conference Paper -
HiMap: Fast and Scalable High-Quality Mapping on CGRA via Hierarchical Abstraction
(2021)Proceedings of the 2021 Design, Automation & Test in Europe (DATE 2021)Coarse-Grained Reconfigurable Array (CGRA) has emerged as a promising hardware accelerator due to the excellent balance among reconfigurability, performance, and energy efficiency. The CGRA performance strongly depends on a high-quality compiler to map the application kernels on the architecture. Unfortunately, the state-of-the-art compilers fall short in generating high quality mapping within an acceptable compilation time, especially ...Conference Paper -
Stitching Weight-Shared Deep Neural Networks for Efficient Multitask Inference on GPU
(2022)2022 19th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)Intelligent personal and home applications demand multiple deep neural networks (DNNs) running on resource-constrained platforms for compound inference tasks, known as multitask inference. To fit multiple DNNs into low-resource devices, emerging techniques resort to weight sharing among DNNs to reduce their storage. However, such reduction in storage fails to translate into efficient execution on common accelerators such as GPUs. Most DNN ...Conference Paper -
Automated Pollen Detection with an Affordable Technology
(2020)Proceedings of the 2020 International Conference on Embedded Wireless Systems and NetworksAirborne pollen cause seasonal allergies and the number of people affected increases yearly due to global warming and urbanization. Governmental pollen sensing stations are sampling traps which require manual pollen identification and counting by trained personnel in the lab. In the past years, a number of researchers and startups started working towards automated pollen measurements by exploring a wide range of techniques. Many solutions ...Conference Paper -
MapTransfer: Urban air quality map generation for downscaled sensor deployments
(2020)2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI)Dense deployments of commodity air quality sensors have proven effective to provide spatially-resolved information on urban air pollution in real-time. However, long-term operation of a dense sensor deployment incurs enormous maintenance expenses and efforts. A cost-effective alternative is to first collect measurements with an initial dense deployment and then rely on a small subset of sensors for air quality map generation. To avoid ...Conference Paper -
Increased reproducibility and comparability of data leak evaluations using ExOT
(2020)2020 Design, Automation and Test in Europe Conference and Exhibition (DATE)As computing systems are increasingly shared among different users or application domains, researchers have intensified their efforts to detect possible data leaks. In particular, many investigations highlight the vulnerability of systems w.r.t. covert and side channel attacks. However, the effort required to reproduce and compare different results has proven to be high. Therefore, we present a novel methodology for covert channel evaluation. ...Conference Paper -
Rethinking Pruning for Accelerating Deep Inference At the Edge
(2020)KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningThere is a growing trend to deploy deep neural networks at the edge for high-accuracy, real-time data mining and user interaction. Applications such as speech recognition and language understanding often apply a deep neural network to encode an input sequence and then use a decoder to generate the output sequence. A promising technique to accelerate these applications on resource-constrained devices is network pruning, which compresses ...Conference Paper -
Pollen video library for benchmarking detection, classification, tracking and novelty detection tasks: Dataset
(2020)Proceedings of the Third Workshop on Data: Acquisition To AnalysisAutomatic pollen sensing is important to understand the local distribution of pollen in urban environments and to give personalized advice to the citizens suffering from seasonal pollen allergies to help milder the symptoms. We present a challenging data set of labeled sequential pollen images recorded with an off-the-shelf microscope to test and improve on a variety of tasks, such as pollen detection, classification, tracking, and novelty ...Conference Paper