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

A Wide Linear Dynamic Range Image Sensor Based on Asynchronous Self-Reset and Tagging of Saturation Events

We report a high dynamic range (HDR) image sensor with a linear response that overcomes some of the limitations of sensors with pixels with self-reset operation. It operates similar to an active pixel sensor, but its pixels have a novel asynchronous event-based overflow detection mechanism. Whenever the pixel voltages at the integration capacitance reach a programmable threshold, the pixels self-reset and send out asynchronously an event indicating this. At the end of the integration period, the voltage at the integration capacitance is digitized and readout. Combining this information with the number of events fired by each pixel, it is possible to render linear HDR images. Event operation is transparent to the final user. There is no limitation for the number of self-resets of each pixel. The output data format is compatible with frame-based devices. The sensor was fabricated in the AMS 0.18- μm HV technology. A detailed system description and experimental results are provided in this paper. The sensor can render images with an intra-scene dynamic range of up to 130 dB with linear outputs. The pixels' pitch is 25 μm and the sensor power consumption is 58.6 mW.Universidad de Cádiz PR2016-072Ministerio de Economía y Competitividad TEC2015-66878-C3-1-RJunta de Andalucía TIC 2012-2338Office of Naval Research (USA) N00014141035

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world

Neuromorphic-P2M: Processing-in-Pixel-in-Memory Paradigm for Neuromorphic Image Sensors

Edge devices equipped with computer vision must deal with vast amounts of sensory data with limited computing resources. Hence, researchers have been exploring different energy-efficient solutions such as near-sensor processing, in-sensor processing, and in-pixel processing, bringing the computation closer to the sensor. In particular, in-pixel processing embeds the computation capabilities inside the pixel array and achieves high energy efficiency by generating low-level features instead of the raw data stream from CMOS image sensors. Many different in-pixel processing techniques and approaches have been demonstrated on conventional frame-based CMOS imagers, however, the processing-in-pixel approach for neuromorphic vision sensors has not been explored so far. In this work, we for the first time, propose an asynchronous non-von-Neumann analog processing-in-pixel paradigm to perform convolution operations by integrating in-situ multi-bit multi-channel convolution inside the pixel array performing analog multiply and accumulate (MAC) operations that consume significantly less energy than their digital MAC alternative. To make this approach viable, we incorporate the circuit's non-ideality, leakage, and process variations into a novel hardware-algorithm co-design framework that leverages extensive HSpice simulations of our proposed circuit using the GF22nm FD-SOI technology node. We verified our framework on state-of-the-art neuromorphic vision sensor datasets and show that our solution consumes ~2x lower backend-processor energy while maintaining almost similar front-end (sensor) energy on the IBM DVS128-Gesture dataset than the state-of-the-art while maintaining a high test accuracy of 88.36%.Comment: 17 pages, 11 figures, 2 table

Interfacing of neuromorphic vision, auditory and olfactory sensors with digital neuromorphic circuits

The conventional Von Neumann architecture imposes strict constraints on the development of intelligent adaptive systems. The requirements of substantial computing power to process and analyse complex data make such an approach impractical to be used in implementing smart systems. Neuromorphic engineering has produced promising results in applications such as electronic sensing, networking architectures and complex data processing. This interdisciplinary field takes inspiration from neurobiological architecture and emulates these characteristics using analogue Very Large Scale Integration (VLSI). The unconventional approach of exploiting the non-linear current characteristics of transistors has aided in the development of low-power adaptive systems that can be implemented in intelligent systems. The neuromorphic approach is widely applied in electronic sensing, particularly in vision, auditory, tactile and olfactory sensors. While conventional sensors generate a huge amount of redundant output data, neuromorphic sensors implement the biological concept of spike-based output to generate sparse output data that corresponds to a certain sensing event. The operation principle applied in these sensors supports reduced power consumption with operating efficiency comparable to conventional sensors. Although neuromorphic sensors such as Dynamic Vision Sensor (DVS), Dynamic and Active pixel Vision Sensor (DAVIS) and AEREAR2 are steadily expanding their scope of application in real-world systems, the lack of spike-based data processing algorithms and complex interfacing methods restricts its applications in low-cost standalone autonomous systems. This research addresses the issue of interfacing between neuromorphic sensors and digital neuromorphic circuits. Current interfacing methods of these sensors are dependent on computers for output data processing. This approach restricts the portability of these sensors, limits their application in a standalone system and increases the overall cost of such systems. The proposed methodology simplifies the interfacing of these sensors with digital neuromorphic processors by utilizing AER communication protocols and neuromorphic hardware developed under the Convolution AER Vision Architecture for Real-time (CAVIAR) project. The proposed interface is simulated using a JAVA model that emulates a typical spikebased output of a neuromorphic sensor, in this case an olfactory sensor, and functions that process this data based on supervised learning. The successful implementation of this simulation suggests that the methodology is a practical solution and can be implemented in hardware. The JAVA simulation is compared to a similar model developed in Nengo, a standard large-scale neural simulation tool. The successful completion of this research contributes towards expanding the scope of application of neuromorphic sensors in standalone intelligent systems. The easy interfacing method proposed in this thesis promotes the portability of these sensors by eliminating the dependency on computers for output data processing. The inclusion of neuromorphic Field Programmable Gate Array (FPGA) board allows reconfiguration and deployment of learning algorithms to implement adaptable systems. These low-power systems can be widely applied in biosecurity and environmental monitoring. With this thesis, we suggest directions for future research in neuromorphic standalone systems based on neuromorphic olfaction

An Event-Based Neurobiological Recognition System with Orientation Detector for Objects in Multiple Orientations

A new multiple orientation event-based neurobiological recognition system is proposed by integrating recognition and tracking function in this paper, which is used for asynchronous address-event representation (AER) image sensors. The characteristic of this system has been enriched to recognize the objects in multiple orientations with only training samples moving in a single orientation. The system extracts multi-scale and multi-orientation line features inspired by models of the primate visual cortex. An orientation detector based on modified Gaussian blob tracking algorithm is introduced for object tracking and orientation detection. The orientation detector and feature extraction block work in simultaneous mode, without any increase in categorization time. An addresses lookup table (addresses LUT) is also presented to adjust the feature maps by addresses mapping and reordering, and they are categorized in the trained spiking neural network. This recognition system is evaluated with the MNIST dataset which have played important roles in the development of computer vision, and the accuracy is increase owing to the use of both ON and OFF events. AER data acquired by a DVS are also tested on the system, such as moving digits, pokers, and vehicles. The experimental results show that the proposed system can realize event-based multi-orientation recognition.The work presented in this paper makes a number of contributions to the event-based vision processing system for multi-orientation object recognition. It develops a new tracking-recognition architecture to feedforward categorization system and an address reorder approach to classify multi-orientation objects using event-based data. It provides a new way to recognize multiple orientation objects with only samples in single orientation

Invariant feature extraction from event based stimuli

We propose a novel architecture, the event-based GASSOM for learning and extracting invariant representations from event streams originating from neuromorphic vision sensors. The framework is inspired by feed-forward cortical models for visual processing. The model, which is based on the concepts of sparsity and temporal slowness, is able to learn feature extractors that resemble neurons in the primary visual cortex. Layers of units in the proposed model can be cascaded to learn feature extractors with different levels of complexity and selectivity. We explore the applicability of the framework on real world tasks by using the learned network for object recognition. The proposed model achieve higher classification accuracy compared to other state-of-the-art event based processing methods. Our results also demonstrate the generality and robustness of the method, as the recognizers for different data sets and different tasks all used the same set of learned feature detectors, which were trained on data collected independently of the testing data.Comment: 6 page

A Selective Change Driven System for High-Speed Motion Analysis

Vision-based sensing algorithms are computationally-demanding tasks due to the large amount of data acquired and processed. Visual sensors deliver much information, even if data are redundant, and do not give any additional information. A Selective Change Driven (SCD) sensing system is based on a sensor that delivers, ordered by the magnitude of its change, only those pixels that have changed most since the last read-out. This allows the information stream to be adjusted to the computation capabilities. Following this strategy, a new SCD processing architecture for high-speed motion analysis, based on processing pixels instead of full frames, has been developed and implemented into a Field Programmable Gate-Array (FPGA). The programmable device controls the data stream, delivering a new object distance calculation for every new pixel. The acquisition, processing and delivery of a new object distance takes just 1.7 μ s. Obtaining a similar result using a conventional frame-based camera would require a device working at roughly 500 Kfps, which is far from being practical or even feasible. This system, built with the recently-developed 64 × 64 CMOS SCD sensor, shows the potential of the SCD approach when combined with a hardware processing system

Complexity Analysis of Iterative Basis Transformations Applied to Event-Based Signals

This paper introduces an event-based methodology to perform arbitrary linear basis transformations that encompass a broad range of practically important signal transforms, such as the discrete Fourier transform (DFT) and the discrete wavelet transform (DWT). We present a complexity analysis of the proposed method, and show that the amount of required multiply-and-accumulate operations is reduced in comparison to frame-based method in natural video sequences, when the required temporal resolution is high enough. Experimental results on natural video sequences acquired by the asynchronous time-based neuromorphic image sensor (ATIS) are provided to support the feasibility of the method, and to illustrate the gain in computation resources