Asynchronous Address Event Representation Research Articles

A neuromorphic core based on threshold switching memristor with asynchronous address event representation circuits

A neuromorphic core based on threshold switching memristor with asynchronous address event representation circuits

A Digital CMOS Silicon Photomultiplier Using Perimeter Gated Single Photon Avalanche Diodes With Asynchronous AER Readout

This paper presents a new architecture for a perimeter gated single photon avalanche diodes (PGSPADs) based CMOS digital silicon photomultiplier (SiPM) with fully digital asynchronous address event representation (AER) readout. A PGSPAD is a single photon avalanche diode with an additional polysilicon gate and mitigates the perimeter edge breakdown, modulates the noise floor, dynamic range, and sensitivity of the device. Spatial and temporal data compression techniques in the SiPM pixel improve dead time, and reduce electronics. Fill factor is improved using a compact, low power analog counter. The dead time improves by 25% with temporal compression improving by a factor of 10 for the pixel microcell. A $4\times 4$ pixel array in a standard $0.5~\mu \text{m}$ CMOS process is fully characterized for the dark counts as function of gate and excess bias as well as varying optical intensity. The array-level dynamic range is 142 dB.

Spike-Based Readout of POSFET Tactile Sensors

We present a low-power compact circuit for the event-driven readout of tactile sensors. The taxel is based on the POSFET device, a sensotronic unit where a piezo-electric material is deposited on the gate of a transistor, integrated with a Leaky-Integrate and Fire neuron circuit. This device encodes the applied force with trains of digital pulses, using the asynchronous Address Event Representation protocol. The instantaneous output spiking frequency is directly proportional to the force applied to the taxel. We show a full characterization of the device as well as quantitative measurements from a prototype fabricated on AMS CMOS 350-nm process. The taxel can reliably distinguish applied forces from 0.1 N up to 1.5 N.

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 increased owing to the use of both ON and OFF events. AER data acquired by a dynamic vision senses (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.

HFirst: A Temporal Approach to Object Recognition.

This paper introduces a spiking hierarchical model for object recognition which utilizes the precise timing information inherently present in the output of biologically inspired asynchronous address event representation (AER) vision sensors. The asynchronous nature of these systems frees computation and communication from the rigid predetermined timing enforced by system clocks in conventional systems. Freedom from rigid timing constraints opens the possibility of using true timing to our advantage in computation. We show not only how timing can be used in object recognition, but also how it can in fact simplify computation. Specifically, we rely on a simple temporal-winner-take-all rather than more computationally intensive synchronous operations typically used in biologically inspired neural networks for object recognition. This approach to visual computation represents a major paradigm shift from conventional clocked systems and can find application in other sensory modalities and computational tasks. We showcase effectiveness of the approach by achieving the highest reported accuracy to date (97.5% ± 3.5%) for a previously published four class card pip recognition task and an accuracy of 84.9% ± 1.9% for a new more difficult 36 class character recognition task.

A <formula formulatype="inline"><tex Notation="TeX">${0.35}~\mu{\rm m}$</tex></formula> Sub-ns Wake-up Time ON-OFF Switchable LVDS Driver-Receiver Chip I/O Pad Pair for Rate-Dependent Power Saving in AER Bit-Serial Links

This paper presents a low power switchable current mode driver/receiver I/O pair for high speed serial transmission of asynchronous address event representation (AER) information. The sparse nature of AER packets (also called events) allows driver/receiver bias currents to be switched off to save power. The on/off times must be lower than the bit time to minimize the latency introduced by the switching mechanism. Using this technique, the link power consumption can be scaled down with the event rate without compromising the maximum system throughput. The proposed technique has been implemented on a typical push/pull low voltage differential signaling (LVDS) circuit, but it can easily be extended to other widely used current mode standards, such as current mode logic (CML) or low-voltage positive emitter-coupled logic (LVPECL). A proof of concept prototype has been fabricated in 0.35 μm CMOS incorporating the proposed driver/receiver pair along with a previously reported switchable serializer/deserializer scheme. At a 500 Mbps bit rate, the maximum event rate is 11 Mevent/s for 32-bit events. In this situation, current consumption is 7.5 mA and 9.6 mA for the driver and receiver, respectively, while differential voltage amplitude is ±300 mV. However, if event rate is lower than 20-30 Kevent/s, current consumption has a floor of 270 μA for the driver and 570 μA for the receiver. The measured ON/OFF switching times are in the order of 1 ns. The serial link could be operated at up to 710 Mbps bit rate, resulting in a maximum 32-bit event rate of 15 Mevent/s . This is the same peak event rate as that obtained with the same SerDes circuits and a non-switched driver/receiver pair.

Asynchronous Event-Based Visual Shape Tracking for Stable Haptic Feedback in Microrobotics

Micromanipulation systems have recently been receiving increased attention. Teleoperated or automated micromanipulation is a challenging task due to the need for high-frequency position or force feedback to guarantee stability. In addition, the integration of sensors within micromanipulation platforms is complex. Vision is a commonly used solution for sensing; unfortunately, the update rate of the frame-based acquisition process of current available cameras cannot ensure-at reasonable costs-stable automated or teleoperated control at the microscale level, where low inertia produces highly unreachable dynamic phenomena. This paper presents a novel vision-based microrobotic system combining both an asynchronous address event representation silicon retina and a conventional frame-based camera. Unlike frame-based cameras, recent artificial retinas transmit their outputs as a continuous stream of asynchronous temporal events in a manner similar to the output cells of a biological retina, enabling high update rates. This paper introduces an event-based iterative closest point algorithm to track a microgripper's position at a frequency of 4 kHz. The temporal precision of the asynchronous silicon retina is used to provide a haptic feedback to assist users during manipulation tasks, whereas the frame-based camera is used to retrieve the position of the object that must be manipulated. This paper presents the results of an experiment on teleoperating a sphere of diameter around 50 μm using a piezoelectric gripper in a pick-and-place task.

An Instant-Startup Jitter-Tolerant Manchester-Encoding Serializer/Deserializer Scheme for Event-Driven Bit-Serial LVDS Interchip AER Links

This paper presents a serializer/deserializer scheme for asynchronous address event representation (AER) bit-serial interchip communications. Each serial AER (sAER) link uses four wires: a micro strip pair for low voltage differential signaling (LVDS) and two handshaking lines. Each event is represented by a 32-bit word. Two extra preamble bits are used for alignment. Transmission clock is embedded in the data using Manchester encoding. As opposed to conventional LVDS links, the presented approach allows to stop physical communication between data events, so that no “comma” characters need to be transmitted during these pauses. As soon as a new event needs to be transmitted, the link recovers immediately thanks to a built-in control voltage memorization circuit. As a result, power consumption of the serializer and deserializer circuits is proportional to data event rate. The approach is also highly tolerant to clock jitter, due to the asynchronous nature and the Manchester encoding. A chip test prototype has been fabricated in standard 0.35 μm CMOS including a pair of Serializer and Deserializer circuits. Maximum measured event transmission rate is 15 Meps (mega events per second) for 32-bit events, with a maximum bit transmission speed of 670 Mbps (mega bits per second).