Live Demonstration: Multiplexing AER Asynchronous Channels over LVDS Links with Flow-Control and Clock- Correction for Scalable Neuromorphic Systems

In this live demonstration we exploit the use of a serial link for fast asynchronous communication in massively parallel processing platforms connected to a DVS for realtime implementation of bio-inspired vision processing on spiking neural networks

A Bio-Inspired Vision Sensor With Dual Operation and Readout Modes

This paper presents a novel event-based vision sensor with two operation modes: intensity mode and spatial contrast detection. They can be combined with two different readout approaches: pulse density modulation and time-to-first spike. The sensor is conceived to be a node of an smart camera network made up of several independent an autonomous nodes that send information to a central one. The user can toggle the operation and the readout modes with two control bits. The sensor has low latency (below 1 ms under average illumination conditions), low power consumption (19 mA), and reduced data flow, when detecting spatial contrast. A new approach to compute the spatial contrast based on inter-pixel event communication less prone to mismatch effects than diffusive networks is proposed. The sensor was fabricated in the standard AMS4M2P 0.35-um process. A detailed system-level description and experimental results are provided.Office of Naval Research (USA) N00014-14-1-0355Ministerio de Economía y Competitividad TEC2012- 38921-C02-02, P12-TIC-2338, IPT-2011-1625-43000

Development of a bio-inspired vision system for mobile micro-robots

In this paper, we present a new bio-inspired vision system for mobile micro-robots. The processing method takes inspiration from vision of locusts in detecting the fast approaching objects. Research suggested that locusts use wide field visual neuron called the lobula giant movement detector to respond to imminent collisions. We employed the locusts' vision mechanism to motion control of a mobile robot. The selected image processing method is implemented on a developed extension module using a low-cost and fast ARM processor. The vision module is placed on top of a micro-robot to control its trajectory and to avoid obstacles. The observed results from several performed experiments demonstrated that the developed extension module and the inspired vision system are feasible to employ as a vision module for obstacle avoidance and motion control

Bio-inspired vision-based leader-follower formation flying in the presence of delays

Flocking starlings at dusk are known for the mesmerizing and intricate shapes they generate, as well as how fluid these shapes change. They seem to do this effortlessly. Real-life vision-based flocking has not been achieved in micro-UAVs (micro Unmanned Aerial Vehicles) to date. Towards this goal, we make three contributions in this paper: (i) we used a computational approach to develop a bio-inspired architecture for vision-based Leader-Follower formation flying on two micro-UAVs. We believe that the minimal computational cost of the resulting algorithm makes it suitable for object detection and tracking during high-speed flocking; (ii) we show that provided delays in the control loop of a micro-UAV are below a critical value, Kalman filter-based estimation algorithms are not required to achieve Leader-Follower formation flying; (iii) unlike previous approaches, we do not use external observers, such as GPS signals or synchronized communication with flock members. These three contributions could be useful in achieving vision-based flocking in GPS-denied environments on computationally-limited agents

Night vision obstacle detection and avoidance based on Bio-Inspired Vision Sensors

Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). However, the detection of obstacles especially during night-time is still a challenging task since the lighting conditions are not sufficient for traditional cameras to function properly. Therefore, we exploit the powerful attributes of event-based cameras to perform obstacle detection in low lighting conditions. Event cameras trigger events asynchronously at high output temporal rate with high dynamic range of up to 120 $dB$. The algorithm filters background activity noise and extracts objects using robust Hough transform technique. The depth of each detected object is computed by triangulating 2D features extracted utilising LC-Harris. Finally, asynchronous adaptive collision avoidance (AACA) algorithm is applied for effective avoidance. Qualitative evaluation is compared using event-camera and traditional camera.Comment: Accepted to IEEE SENSORS 202

Digital desing for neuroporphic bio-inspired vision processing.

Artificial Intelligence (AI) is an exciting technology that flourished in this century. One of the goals for this technology is to give learning ability to computers. Currently, machine intelligence surpasses human intelligence in specific domains. Besides some conventional machine learning algorithms, Artificial Neural Networks (ANNs) is arguably the most exciting technology that is used to bring this intelligence to the computer world. Due to ANN’s advanced performance, increasing number of applications that need kind of intelligence are using ANN. Neuromorphic engineers are trying to introduce bio-inspired hardware for efficient implementation of neural networks. This hardware should be able to simulate a vast number of neurons in real-time with complex synaptic connectivity while consuming little power. The work that has been done in this thesis is hardware oriented, so it is necessary for the reader to have a good understanding of the hardware that is used for developments in this thesis. In this chapter, we provide a brief overview of the hardware platforms that are used in this thesis. Afterward, we explain briefly the contributions of this thesis to the bio-inspired processing research line

Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars

Event cameras are bio-inspired vision sensors that naturally capture the dynamics of a scene, filtering out redundant information. This paper presents a deep neural network approach that unlocks the potential of event cameras on a challenging motion-estimation task: prediction of a vehicle's steering angle. To make the best out of this sensor-algorithm combination, we adapt state-of-the-art convolutional architectures to the output of event sensors and extensively evaluate the performance of our approach on a publicly available large scale event-camera dataset (~1000 km). We present qualitative and quantitative explanations of why event cameras allow robust steering prediction even in cases where traditional cameras fail, e.g. challenging illumination conditions and fast motion. Finally, we demonstrate the advantages of leveraging transfer learning from traditional to event-based vision, and show that our approach outperforms state-of-the-art algorithms based on standard cameras.Comment: 9 pages, 8 figures, 6 tables. Video: https://youtu.be/_r_bsjkJTH

Bio-inspired vision mimetics towards next generation collision avoidance automation

The current “deep learning + large-scale data + strong supervised labeling” technology framework of collision avoidance for ground robots and aerial drones is becoming saturated. Its development gradually faces challenges from real open-scene applications, including small data, weak annotation, and cross-scene. Inspired by the neural structure and processes underlying human cognition (e.g., human visual, auditory, and tactile systems) and the knowledge learned from daily driving tasks, such as, a high-level cognitive system is developed for integrating collision sensing and collision avoidance. This bio-inspired cognitive approach takes the advantages of good robustness, high self-adaptability, and low computation consumption in practical driving scenes