Call for Participation: Deep Learning Comes to ESTIMedia 2016 Special Session
CALL FOR PARTICIPATION
IEEE ESTIMedia 2016 is going to have a Special Session on Deep Learning and its applications to Multimedia
Special Session Title: Implementation of Deep Neural Networks and Its Applications
Organizer: Yiran Chen, University of Pittsburgh
Presentation 1
Title: Efficient Neural Computing using Cellular Array of Magneto-Metallic Neurons and Synapses
Speaker: Kaushik Roy, Purdue University
Abstract: Brain inspired computing models like Artificial Neural Networks (ANN) and Spiking Neural Networks (SNN) have emerged as one of the powerful tools for pattern recognition and classification problems. Nano devices emulating the functionality of neurons and synapses are a crucial requirement for such neuromorphic computing platforms. Recent experiments on lateral spin valves (LSV) have demonstrated the switching of nano-magnets when the net spin potential in the non-magnetic channel due to injected spin-polarized current exceeds a certain threshold, thereby emulating the biological neuron. Programmable domain wall strips can be interfaced with such "spin-neurons" to inject weighted spin-polarized current in the channel to mimic the synaptic functionality. The low resistance, magneto-metallic neurons operate at a small terminal of ~20mV, while performing analog computation upon inputs leading to the possibility of ultra low power, low-voltage ANN based neuromorphic computing. On the other hand, a more biologically realistic computing model in comparison to ANNs, SNNs perform unsupervised learning by spike transmission and require the online programming of synapses based on the temporal information of spikes. Programmable resistive synapses based on ferromagnet-heavy metal hetero-structure offers the possibility of implementing Spike Timing Dependent Plasticity mechanisms by utilizing the highly energy-efficient spin-orbit torque. Results indicate that the proposed design schemes can achieve ~100X reduction in computation energy compared to the state of art CMOS designs.
Presentation 2
Title: Reflections of Deep Networks in Multimedia: From an Industry Perspective
Speaker: Liangliang Cao, Yahoo Labs
Abstract: Deep neural network has become a hot topic in recent years. It opens a door to big jumps of recognition accuracy and dramatic speedup of model evaluation. In the multimedia field, we have seen a number of cases where deep neural networks have created huge value to company's business. This talk will first introduce some fundamental concepts of deep learning, then analyze a few success projects in industry, and finally discuss some future challenges of deep neural networks.
Presentation 3
Title: Real-Time Pedestrian Detection with Convolutional Neural Network on Customized Hardware
Speaker: Yu Wang, DeePhi Tech. & Tsinghua University
Abstract: Pedestrian detection is a core problem in computer vision and plays an important role in many real-word applications such as Advanced Driving Assistance System (ADAS) and video surveillance. The adoption of Convolutional Neural Network (CNN) on pedestrian detection has significantly increased the accuracy of pedestrian detection but the complexity of CNN makes it hard to run on embedded device in real time. In this paper, we present a novel customized hardware platform on embedded FPGA for pedestrian detection. The platform runs in real-time with power less than 4 watt.
Presentation 4
Title: An Efficient Deep Convolutional Network Architecture for Road Scene Understanding
Speaker: Yiran Chen, University of Pittsburgh
Abstract: Analyzing road scenes using cameras could have a crucial impact in many domains, such as autonomous driving, personal navigation and mapping of large scale environments. It requires the ability to model appearance (road, building), shape (cars, pedestrians) and understand the spatial-relationship (context) between different classes such as road and side-walk. In typical road scenes, the majority of the pixels belong to large classes such as road, building and hence the network must produce smooth segmentations. We present a deep convolutional neural network based segmentation engine to delineate moving and other objects based on their shape despite their small size, hence retain boundary information in the extracted image representation. From a computational perspective, optimizations on network architecture are demonstrated to be efficient in terms of both memory and computation time during inference.
Technical Program Co-Chairs
- Muhammad Shafique, KIT, Germany
- Sander Stuijk, TUE, The Netherlands
General Chair
- Hyunok Oh, Hanyang University, Korea