You Only Look Once: Unified, Real-Time Object Detection

by Jaemin Kang on 2020-04-14 21:20:28

Date: 2020. 04.17 (Fri) 15:00 Locate: EB5. 533 and Zoom Presenter: Jaemin Kang Title: You Only Look Once: Unified, Real-Time Object Detection Author: Joseph Redmon, Santosh Divvala, Ross Girshick, A Li Farhadi Abstract: We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of ... Continue reading →


Tightening Contention Delays While Scheduling Parallel Applications on Multi-core Architectures

by Sihyeong Park on 2020-04-06 09:42:43

Date: 2020. 04.10 (Fri) 15:00 Locate: EB5. 533 and Zoom Presenter: Sihyeong Park Title: Tightening Contention Delays While Scheduling Parallel Applications on Multi-core Architectures Author: Benjamin Rouxel, Steven  Derrien, Isabelle  Puaut Abstract: Multi-core systems are increasingly interesting candidates for executing parallel real-time applications, in avionic, space or automotive industries, as they provide both computing capabilities and power efficiency. However, ensuring that timing constraints are met on such platforms is challenging, because some hardware resources are shared between cores. Assuming worst-case contentions when analyzing the schedulability of applications may result in systems mistakenly declared unschedulable, although the worst-case level of contentions can never occur in practice. In this paper, we present two contention-aware scheduling strategies that produce a time-triggered schedule of the application’s tasks. Based on knowledge of ... Continue reading →


Portable and Configurable Implementation of ARINC-653 Temporal Partitioing for Small Civilian UAVs

by Jihun Bae on 2020-02-24 11:25:10

Date: 2020. 02. 24 (Mon) 15:00 Locate: EB5. 533 Presenter: Jihun Bae Title: Portable and Configurable Implementation of ARINC-653 Temporal Partitioing for Small Civilian UAVs Author: HYUN-CHUL JO JOO-KWANG PARK, HYUN-WOOK JIN, HYUNG-SIK YOON, AND SANG HUN LEE Abstract: The ARINC-653 standard denes temporal partitioning that enables multiple avionics applications to execute independently from each other without interference in terms of CPU resources. Though partitioning has been mainly discussed from the viewpoint of manned aircraft, it can also efficiently integrate multiple applications on civilian Unmanned Aerial Vehicles (UAVs) that have even severer limitations on size, weight, power, and cost. In order to employ ARINC-653 temporal partitioning to civilian UAVs, its implementation must be flexible enough to be applied to diverse run-time software environments and computing hardware platforms. In this paper, we suggest a portable and configurable implementation ... Continue reading →


Environmental Sound Classification on Microcontrollers using Convolutional Neural Networks

by Juwon You on 2020-02-24 09:51:17

Date: 2020. 02. 24 (Mon) 15:00 Locate: EB5. 533 Presenter: Jaemin Kang, Juwon You Title: Environmental Sound Classification on Microcontrollers using Convolutional Neural Networks Author: Jon Nordy Abstract: Noise is a growing problem in urban areas, and according to the WHO is the second environmental cause of health problems in Europe. Noise monitoring using Wireless Sensor Networks are being applied in order to understand and help mitigate these noise problems. It is desirable that these sensor systems, in addition to logging the sound level, can indicate what the likely sound source is. However, transmitting audio to a cloud system for classification is energy-intensive and may cause privacy issues. It is also critical for widespread adoption and dense sensor coverage that individual sensor nodes are low-cost. Therefore we propose to perform the noise classification on the sensor node, using a low-cost microcontroller. Several Convolutional Neural Networks ... Continue reading →


by Juwon You on 2020-02-24 09:35:22

                Continue reading →


Split-CNN: Splitting Window-based Operations in CNN for Memory System Optimization

by Donghee Ha on 2020-01-23 14:43:44

Date: 2020. 01. 20 (Mon) 15:00 Locate: EB5. 533 Presenter: Donghee Ha Title: Split-CNN: Splitting Window-based Operations in Convolutional Neural Networks for Memory System Optimization Author: Tian Jin, Seokin Hong Abstract: We present an interdisciplinary study to tackle the memory bottleneck of training deep convolutional neural networks (CNN). Firstly, we introduce Split Convolutional Neural Network (Split-CNN) that is derived from the automatic transformation of the state-of-the-art CNN models. The main distinction between Split-CNN and regular CNN is that Split-CNN splits the input images into small patches and operates on these patches independently before entering later stages of the CNN model. Secondly, we propose a novel heterogeneous memory management system (HMMS) to utilize the memory-friendly properties of Split-CNN. Through experiments, we demonstrate that Split-CNN achieves significantly higher training scalability by dramatically reducing the memory ... Continue reading →


Hips Do Lie! A Position-Aware Mobile Fall Detection System

by Jinyoung Choi on 2020-01-19 18:22:58

Date: 2020. 01. 20 (Mon) 15:00 Locate: EB5. 533 Presenter: Jinyoung Choi Title: Hips Do Lie! A Position-Aware Mobile Fall Detection System 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom) Author: Christian Krupitzer, Timo Sztyler, Janick Edinger, Martin Breitvach, Heiner Stuckenschmidt, Christian Becker  Abstract: Ambient Assisted Living using mobile device sensors is an active area of research in pervasive computing. Multiple approaches have shown that wearable sensors perform very well and distinguish falls reliably from Activities of Daily Living. However, these systems are tested in a controlled environment and are optimized for a given set of sensor types, sensor positions, and subjects. In this work, we propose a self-adaptive pervasive fall detection approach that is robust to the heterogeneity of real life situations. Therefore, we combine sensor data of four publicly available datasets, covering about 100 subjects, ... Continue reading →


Cache Locking Content Selection Algorithms for ARINC-653 Compliant RTOS

by Sihyeong Park on 2020-01-06 17:02:55

Date: 2020. 01. 13 (Mon) 15:00 Locate: EB5. 533 Presenter: Sihyeong Park Title: Cache Locking Content Selection Algorithms for ARINC-653 Compliant RTOS ACM Transactions on Embedded Computing Systems (TECS) 18.5s (2019): 76. Author: Alexy Torres Aurora Dugo, Jean Baptiste Lefoul, Felipe Göhring De Magalhães, Dahman Assal, Gabriela  Nicolescu  Abstract: Avionic software is the subject of stringent real time, determinism and safety constraints. Software designers face several challenges, one of them being the interferences that appear in common situations, such as resource sharing. The interferences introduce non-determinism and delays in execution time. One of the main interference prone resources are cache memories. In single-core processors, caches comprise multiple private levels. This breaks the isolation principle imposed by avionic standards, such as the ARINC-653. This standard defines partitioned architectures where one partition should ... Continue reading →


MOSAIC: Heterogeneity-, Communication-, and Constraint-Aware Model Slicing and Execution...

by Jinse Kwon on 2020-01-02 18:04:34

Date : 2020. 01. 06 (Mon) 15:00 Locate : EB5. 533 Presenter : Jinse Kwon   Title : MOSAIC: Heterogeneity-, Communication-, and Constraint-Aware Model Slicing and Execution for Accurate and Efficient Inference Author : Myeonggyun Han ; Jihoon Hyun ; Seongbeom Park ; Jinsu Park ; Woongki Baek (UNIST, Republic of Korea)   Abstract : Heterogeneous embedded systems have surfaced as a promising solution for accurate and efficient deep-learning inference on mobile devices. Despite extensive prior works, it still remains unexplored to investigate the system-software support that efficiently executes inference workloads by judiciously considering their performance and energy heterogeneity, communication overheads, and constraints. To bridge this gap, we propose MOSAIC, heterogeneity-, communication-, and constraint-aware model slicing and execution for accurate and efficient inference on heterogeneous embedded systems. MOSAIC generates the ... Continue reading →


Achieving Lossless Accuracy with Lossy Programming for Efficient Neural-Network Training

by Donghee Ha on 2019-11-06 17:14:20

Date: 2019. 11. 06 (Thu) 18:00 Locate: EB5. 533 Presenter: Donghee Ha Title: Achieving Lossless Accuracy with Lossy Programming for Efficient Neural-Network Training on NVM-Based Systems Author: Wei-Chen Wang, Yuan-Hao Chang, Tei-Wei Kuo, Chien-Chung Ho, Yu-Ming Chang and Hung-Sheng Chang   Abstract: Neural networks over conventional computing platforms are heavily restricted by the data volume and performance concerns. While non-volatile memory offers potential solutions to data volume issues, challenges must be faced over performance issues, especially with asymmetric read and write performance. Beside that, critical concerns over endurance must also be resolved before non-volatile memory could be used in reality for neural networks. This work addresses the performance and endurance concerns altogether by proposing a data-aware programming scheme. We propose to consider neural network training jointly with respect to the data-flow and data-content points of view. ... Continue reading →


전달세미나 : ESWEEK 2019

by Jinse Kwon on 2019-10-24 13:43:19

Date : 2019. 10. 24 (Wed) 19:30 Locate : EB5. 507 Presenter : Hyungshin Kim, Jinse Kwon Title : Message for ESWEEK 2019 Web : ESWEEK 2019 Continue reading →


Lab Seminar : Oct. 24 7:30 2019 ESWEEK Review

by Hyungshin Kim on 2019-10-23 21:27:45

Hyungshin Kim and Jinse Kwon will review this year's ESWEEK. CASES, CODES+ISSS, EMSOFT will be reviewed. Continue reading →


StreamBox-TZ: Secure Stream Analytics at the Edge with TrustZone

by Sihyeong Park on 2019-10-08 16:13:29

Date: 2019. 10. 10 (Thu) 19:30 Locate: EB5. 507 Presenter: Sihyeong Park Title: StreamBox-TZ: Secure Stream Analytics at the Edge with TrustZone Author: Heejin Park and Shuang Zhai, Purdue ECE; Long Lu, Northeastern University; Felix Xiaozhu Lin, Purdue ECE Abstract: While it is compelling to process large streams of IoT data on the cloud edge, doing so exposes the data to a sophisticated, vulnerable software stack on the edge and hence security threats. To this end, we advocate isolating the data and its computations in a trusted execution environment (TEE) on the edge, shielding them from the remaining edge software stack which we deem untrusted. This approach faces two major challenges: (1) executing high-throughput, low-delay stream analytics in a single TEE, which is constrained by a low trusted computing base (TCB) and limited physical memory; (2) verifying execution of stream analytics as the execution involves untrusted software components on the edge. In ... Continue reading →


Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis

by Jinse Kwon on 2019-09-19 16:06:25

Date : 2019. 09. 04 (Wed) 13:30 Locate : EB5. 533 Presenter : Jinse Kwon   Title : Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis Author : Tal Ben-Nun, Torsten Hoefler (ETH Zurich, Zurich, Switzerland)   Abstract : Deep Neural Networks (DNNs) are becoming an important tool in modern computing applications. Accelerating their training is a major challenge and techniques range from distributed algorithms to low-level circuit design. In this survey, we describe the problem from a theoretical perspective, followed by approaches for its parallelization. We present trends in DNN architectures and the resulting implications on parallelization strategies. We then review and model the different types of concurrency in DNNs: from the single operator, through parallelism in network inference and training, to distributed deep learning. We discuss asynchronous stochastic optimization, distributed system ... Continue reading →


Software fault injection testing of the embedded software of a satellite launch vehicle

by Hyeoksoo Jang on 2019-09-19 15:41:10

Date: 2019. 08. 07 (Wed) 13:00 Locate: EB5. 533 Presenter: Hyeoksoo Jang Title: Software fault injection testing of the embedded software of a satellite launch vehicle Author: Anil Abraham Samuel, Jayalal N., Valsa B., Ignatious C.A., and John P. Zachariah Abstract: The software performing navigation, guidance, control, and mission-sequencing functionalities embedded in the flight computer system (FCS) of a satellite launch vehicle must be highly dependable. The presence of faults in the embedded flight software affects its dependability and may even jeopardize the entire mission, resulting in a huge loss to the space agency concerned. There are many techniques available to achieve high dependability and can be classified under fault avoidance, fault removal and fault tolerance. In the FCS of the Indian Space Research Organization’s (ISRO’s) satellite launch vehicles, all of the above means to achieve dependability are adopted. Fault avoidance and removal ... Continue reading →