Deep Learning for Embedded Systems

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This blog post has recent publications related to Deep Learning for Embedded Systems (e.g. computer systems in toys, biometrics, cars, kitchen equipment, medical equipment such as bionic eyes, etc).

Wikipedia defines Embedded systems as:

    An embedded system is a computer system with a dedicated function within a larger mechanical or electrical system, often with real-time computing constraints.[1][2] It is embedded as part of a complete device often including hardware and mechanical parts. Embedded systems control many devices in common use today.[3] Ninety-eight percent of all microprocessors are manufactured as components of embedded systems

Best regards,
Amund Tveit (WeChat ID: AmundTveit – Twitter: atveit)

Year  Title Author
2017   Six Degree-of-Freedom Localization of Endoscopic Capsule Robots using Recurrent Neural Networks embedded into a Convolutional Neural Network  M Turan, A Abdullah, R Jamiruddin, H Araujo
2017   Two-Bit Networks for Deep Learning on Resource-Constrained Embedded Devices  W Meng, Z Gu, M Zhang, Z Wu
2017   14.1 A 2.9 TOPS/W deep convolutional neural network SoC in FD-SOI 28nm for intelligent embedded systems  G Desoli, N Chawla, T Boesch, S Singh, E Guidetti
2017   Characterization of Symbolic Rules Embedded in Deep DIMLP Networks: A Challenge to Transparency of Deep Learning  G Bologna, Y Hayashi
2017   Moving Object Detection in Heterogeneous Conditions in Embedded Systems  A Garbo, S Quer
2016   Re-architecting the on-chip memory sub-system of machine-learning accelerator for embedded devices  Y Wang, H Li, X Li
2016   DELAROSE: A Case Example of the Value of Embedded Course Content and Assessment in the Workplace  JSG Wells, M Bergin, C Ryan
2016   Neurosurgery Conference Experience Embedded within PCOM’s Clinical and Basic Neuroscience Curriculum: An Active Learning Model  J Okun, S Yocom, M McGuiness, M Bell, D Appelt
2016   Scene Parsing using Inference Embedded Deep Networks  S Bu, P Han, Z Liu, J Han
2016   Improving Deep Learning Accuracy with Noisy Autoencoders Embedded Perturbative Layers  L Xia, X Zhang, B Li
2016   Noise Robust Keyword Spotting Using Deep Neural Networks For Embedded Platforms  R Abdelmoula
2016   14.1 A 126.1 mW real-time natural UI/UX processor with embedded deep-learning core for low-power smart glasses  S Park, S Choi, J Lee, M Kim, J Park, HJ Yoo
2016   A wearable mobility aid for the visually impaired based on embedded 3D vision and deep learning  M Poggi, S Mattoccia
2016   Optimizing convolutional neural networks on embedded platforms with OpenCL  A Lokhmotov, G Fursin
2016   Demonstration Abstract: Accelerating Embedded Deep Learning Using DeepX  ND Lane, S Bhattacharya, P Georgiev, C Forlivesi
2016   Feedback recurrent neural network-based embedded vector and its application in topic model  L Li, S Gan, X Yin
2016   Human Pose Estimation from Depth Images via Inference Embedded Multi-task Learning  K Wang, S Zhai, H Cheng, X Liang, L Lin
2015   Memory Heat Map: Anomaly Detection in Real-Time Embedded Systems Using Memory Behavior  MK Yoon, S Mohan, J Choi, L Sha
2015   Accelerating real-time embedded scene labeling with convolutional networks  L Cavigelli, M Magno, L Benini
2015   Business meeting training on its head: inverted and embedded learning  E Van Praet
2015   CNN optimizations for embedded systems and FFT  A Vasilyev
2015   Learning Socially Embedded Visual Representation from Scratch  S Liu, P Cui, W Zhu, S Yang
2015   Inter-Tile Reuse Optimization Applied to Bandwidth Constrained Embedded Accelerators  M Peemen, B Mesman, H Corporaal
2015   Emotion recognition from embedded bodily expressions and speech during dyadic interactions  PM Müller, S Amin, P Verma, M Andriluka, A Bulling
2015   Incremental extreme learning machine based on deep feature embedded  J Zhang, S Ding, N Zhang, Z Shi
2015   Utilizing deep neural nets for an embedded ECG-based biometric authentication system  A Page, A Kulkarni, T Mohsenin
2015   A scalable and adaptable probabilistic model embedded in an electronic nose for intelligent sensor fusion  CT Tang, CM Huang, KT Tang, H Chen

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