Synthetic Generation of Events for Address-Event-Representation Communications

Address-Event-Representation (AER) is a communications protocol for transferring images between chips, originally developed for bio-inspired image processing systems. Such systems may consist of a complicated hierarchical structure with many chips that transmit images among them in real time, while performing some processing (for example, convolutions). In developing AER based systems it is very convenient to have available some kind of means of generating AER streams from on-computer stored images. In this paper we present a method for generating AER streams in real time from images stored in a computer’s memory. The method exploits the concept of linear feedback shift register random number generators. This method has been tested by software and compared to other possible algorithms for generating AER streams. It has been found that the proposed method yields a minimum error with respect to the ideal situation. A hardware platform that exploits this technique is currently under development

A high-precision current-mode WTA-MAX circuit with multichip capability

This paper presents a circuit design technique suitable for the realization of winner-take-all (WTA), maximum (MAX), looser-take-all (LTA), and minimum (MIN) circuits. The technique presented is based on current replication and comparison. Traditional techniques rely on the matching of an N transistors array, where N is the number of system inputs. This implies that when N increases, as the size of the circuit and the distance between transistors will also increase, transistor matching degradation and loss of precision in the overall system performance will result. Furthermore, when multichip systems are required, the transistor matching is even worse and performance is drastically degraded. The technique presented in this paper does not rely on the proper matching of N transistors, but on the precise replication and comparison of currents. This can be performed by current mirrors with a limited number of outputs. Thus, N can increase without degrading the precision, even if the system is distributed among several chips. Also, the different chips constituting the system can be of different foundries without degrading the overall system precision. Experimental results that attest these facts are presented

An ART1 microchip and its use in multi-ART1 systems

Recently, a real-time clustering microchip neural engine based on the ART1 architecture has been reported. Such chip is able to cluster 100-b patterns into up to 18 categories at a speed of 1.8 μs per pattern. However, that chip rendered an extremely high silicon area consumption of 1 cm2, and consequently an extremely low yield of 6%. Redundant circuit techniques can be introduced to improve yield performance at the cost of further increasing chip size. In this paper we present an improved ART1 chip prototype based on a different approach to implement the most area consuming circuit elements of the first prototype: an array of several thousand current sources which have to match within a precision of around 1%. Such achievement was possible after a careful transistor mismatch characterization of the fabrication process (ES2-1.0 μm CMOS). A new prototype chip has been fabricated which can cluster 50-b input patterns into up to ten categories. The chip has 15 times less area, shows a yield performance of 98%, and presents the same precision and speed than the previous prototype. Due to its higher robustness multichip systems are easily assembled. As a demonstration we show results of a two-chip ART1 system, and of an ARTMAP system made of two ART1 chips and an extra interfacing chip

A modified ART 1 algorithm more suitable for VLSI implementations

This paper presents a modification to the original ART 1 algorithm (Carpenter and Grossberg, 1987a, A massively parallel architecture for a self-organizing neural pattern recognition machine, Computer Vision, Graphics, and Image Processing, 37, 54–115) that is conceptually similar, can be implemented in hardware with less sophisticated building blocks, and maintains the computational capabilities of the originally proposed algorithm. This modified ART 1 algorithm (which we will call here ART 1m) is the result of hardware motivated simplifications investigated during the design of an actual ART 1 chip [Serrano-Gotarredona et al., 1994, Proc. 1994 IEEE Int. Conf. Neural Networks (Vol. 3, pp. 1912–1916); Serrano-Gotarredona and Linares-Barranco, 1996, IEEE Trans. VLSI Systems, (in press)]. The purpose of this paper is simply to justify theoretically that the modified algorithm preserves the computational properties of the original one and to study the difference in behavior between the two approaches

On the design and characterization of femtoampere current-mode circuits

In this paper, we show and validate a reliable circuit design technique based on source voltage shifting for current-mode signal processing down to femtoamperes. The technique involves specific-current extractors and logarithmic current splitters for obtaining on-chip subpicoampere currents. It also uses a special on-chip sawtooth oscillator to monitor and measure currents down to a few femtoamperes. This way, subpicoampere currents are characterized without driving them off chip and requiring expensive instrumentation with complicated low leakage setups. A special current mirror is also introduced for reliably replicating such low currents. As an example, a simple log-domain first-order low-pass filter is Implemented that uses a 100-fF capacitor and a 3.5-fA bias current to achieve a cutoff frequency of 0.5 Hz. A technique for characterizing noise at these currents is also described and verified. Finally, transistor mismatch measurements are provided and discussed. Experimental measurements are shown throughout the paper, obtained from prototypes fabricated in the AMS 0.35-μm three-metal two-poly standard CMOS process.Ministerio de Ciencia y Tecnología TIC-1999-0446-C02-02, FIT-070000-2001-0859, TIC-2000-0406-P4-05, TIC-2002-10878-EEuropean Union IST-2001-3412

Log-domain implementation of complex dynamics reaction-diffusion neural networks

In this paper, we have identified a second-order reaction-diffusion differential equation able to reproduce through parameter setting different complex spatio-temporal behaviors. We have designed a log-domain hardware that implements the spatially discretized version of the selected reaction-diffusion equation. The logarithmic compression of the state variables allows several decades of variation of these state variables within subthreshold operation of the MOS transistors. Furthermore, as all the equation parameters are implemented as currents, they can be adjusted several decades. As a demonstrator, we have designed a chip containing a linear array of ten second-order dynamics coupled cells. Using this hardware, we have experimentally reproduced two complex spatio-temporal phenomena: the propagation of travelling waves and of trigger waves, as well as isolated oscillatory cells.Gobierno de España TIC1999-0446-C02-02Office of Naval Research (USA

Improved Contrast Sensitivity DVS and its Application to Event-Driven Stereo Vision

This paper presents a new DVS sensor with one order of magnitude improved contrast sensitivity over previous reported DVSs. This sensor has been applied to a bio-inspired event-based binocular system that performs 3D event-driven reconstruction of a scene. Events from two DVS sensors are matched by using precise timing information of their ocurrence. To improve matching reliability, satisfaction of epipolar geometry constraint is required, and simultaneously available information on the orientation is used as an additional matching constraint.Ministerio de Economía y Competitividad PRI-PIMCHI-2011-0768Ministerio de Economía y Competitividad TEC2009-10639-C04-01Junta de Andalucía TIC-609

7-decade tuning range CMOS OTA-C sinusoidal VCO

A new operational transconductance amplifier-capacitor (OTA-C) based sinusoidal voltage-controlled oscillator (VCO) has been designed and fabricated, the oscillation frequency of which can be tuned from 74 mHz to 1 MHz. The VCO uses a new OTA whose transconductance is adjusted by using a set of special current mirrors. These current mirrors operate in weak inversion and their gain can be controlled continuously through a gate voltage over many decades. This is the first report of such a wide tuning range for CMOS sinusoidal oscillators. Experimental results are provided

An AER handshake-less modular infrastructure PCB with x8 2.5Gbps LVDS serial links

Nowadays spike-based brain processing emulation is taking off. Several EU and others worldwide projects are demonstrating this, like SpiNNaker, BrainScaleS, FACETS, or NeuroGrid. The larger the brain process emulation on silicon is, the higher the communication performance of the hosting platforms has to be. Many times the bottleneck of these system implementations is not on the performance inside a chip or a board, but in the communication between boards. This paper describes a novel modular Address-Event-Representation (AER) FPGA-based (Spartan6) infrastructure PCB (the AER-Node board) with 2.5Gbps LVDS high speed serial links over SATA cables that offers a peak performance of 32-bit 62.5Meps (Mega events per second) on board-to-board communications. The board allows back compatibility with parallel AER devices supporting up to x2 28-bit parallel data with asynchronous handshake. These boards also allow modular expansion functionality through several daughter boards. The paper is focused on describing in detail the LVDS serial interface and presenting its performance.Ministerio de Ciencia e Innovación TEC2009-10639-C04-02/01Ministerio de Economía y Competitividad TEC2012-37868-C04-02/01Junta de Andalucía TIC-6091Ministerio de Economía y Competitividad PRI-PIMCHI-2011-076

An Event-Driven Multi-Kernel Convolution Processor Module for Event-Driven Vision Sensors

Event-Driven vision sensing is a new way of sensing visual reality in a frame-free manner. This is, the vision sensor (camera) is not capturing a sequence of still frames, as in conventional video and computer vision systems. In Event-Driven sensors each pixel autonomously and asynchronously decides when to send its address out. This way, the sensor output is a continuous stream of address events representing reality dynamically continuously and without constraining to frames. In this paper we present an Event-Driven Convolution Module for computing 2D convolutions on such event streams. The Convolution Module has been designed to assemble many of them for building modular and hierarchical Convolutional Neural Networks for robust shape and pose invariant object recognition. The Convolution Module has multi-kernel capability. This is, it will select the convolution kernel depending on the origin of the event. A proof-of-concept test prototype has been fabricated in a 0.35 m CMOS process and extensive experimental results are provided. The Convolution Processor has also been combined with an Event-Driven Dynamic Vision Sensor (DVS) for high-speed recognition examples. The chip can discriminate propellers rotating at 2 k revolutions per second, detect symbols on a 52 card deck when browsing all cards in 410 ms, or detect and follow the center of a phosphor oscilloscope trace rotating at 5 KHz.Unión Europea 216777 (NABAB)Ministerio de Ciencia e Innovación TEC2009-10639-C04-0