Suchergebnisse

This paper considers the viability of compact low-resolution low-power mini digital-to-analog converters (mini-DACs) for use in large arrays of neural type cells, where programmable weights are required. Transistors are biased in weak inversion in order to yield small currents and low power consumptions, a necessity when building large size arrays. One important drawback of weak inversion operation is poor matching between transistors. The resulting effective precision of a fabricated array of 50 DACs turned out to be 47% (1.1 bits), due to transistor mismatch. However, it is possible to combine them two by two in order to build calibrated DACs, thus compensating for inter-DAC mismatch. It is shown experimentally that the precision can be improved easily by a factor of 10 (4.8% or 4.4 bits), which makes these DACs viable for low-resolution applications such as massive arrays of neural processing circuits. A design methodology is provided, and illustrated through examples, to obtain calibrated mini-DACs of a given target precision. As an example application, we show simulation results of using this technique to calibrate an array of digitally controlled integrate-and-fire neurons. ; Gobierno de España TIC1999-0446-C02-02, TIC2000-0406-P4-05, FIT-07000/2002/921, TIC2002-10878-E ; European Union IST- 2001-34124

We present a neuromorphic cortical-layer processing microchip for address event representation (AER) spike-based processing systems. The microchip computes 2-D convolutions of video information represented in AER format in real time. AER, as opposed to conventional frame-based video representation, describes visual information as a sequence of events or spikes in a way similar to biological brains. This format allows for fast information identification and processing, without waiting to process complete image frames. The neuromorphic cortical-layer processing microchip presented in this paper computes convolutions of programmable kernels over the AER visual input information flow. It not only computes convolutions but also allows for a programmable forgetting rate, which in turn allows for a bio-inspired coincidence detection processing. Kernels are programmable and can be of arbitrary shape and arbitrary size of up to 32 32 pixels. The convolution processor operates on a pixel array of size 32 32, but can process an input space of up to 128 128 pixels. Larger pixel arrays can be directly processed by tiling arrays of chips. The chip receives and generates data in AER format, which is asynchronous and digital. However, its internal operation is based on analog low-current circuit techniques. The paper describes the architecture of the chip and circuits used for the pixels, including calibration techniques to overcome mismatch. Extensive experimental results are provided, describing pixel operation and calibration, convolution processing with and without forgetting, and high-speed recognition experiments like discriminating rotating propellers of different shape rotating at speeds of up to 5000 revolutions per second. ; This work was supported by Spanish Research under Grant TIC2003-08164-C03-01 (SAMANTA) and by the European Union under EU Grant IST-2001-34124 (CAVIAR). The work of R. Serrano-Gotarredona was supported by the Spanish Ministry of Education and Science. ; Peer reviewed

We present a 32 32 pixels contrast retina microchip that provides its output as an address event representation (AER) stream. Spatial contrast is computed as the ratio between pixel photocurrent and a local average between neighboring pixels obtained with a diffuser network. This current-based computation produces an important amount of mismatch between neighboring pixels, because the currents can be as low as a few pico-amperes. Consequently, a compact calibration circuitry has been included to trimm each pixel. Measurements show a reduction in mismatch standard deviation from 57% to 6.6% (indoor light). The paper describes the design of the pixel with its spatial contrast computation and calibration sections. About one third of pixel area is used for a 5-bit calibration circuit. Area of pixel is 58 m 56 m, while its current consumption is about 20 nA at 1-kHz event rate. Extensive experimental results are provided for a prototype fabricated in a standard 0.35- m CMOS process. ; Gobierno de España TIC2003-08164-C03-01, TEC2006-11730-C03-01 ; European Union IST-2001-34124

We present a neuromorphic cortical-layer processing microchip for address event representation (AER) spike-based processing systems. The microchip computes 2-D convolutions of video information represented in AER format in real time. AER, as opposed to conventional frame-based video representation, describes visual information as a sequence of events or spikes in a way similar to biological brains. This format allows for fast information identification and processing, without waiting to process complete image frames. The neuromorphic cortical-layer processing microchip presented in this paper computes convolutions of programmable kernels over the AER visual input information flow. It not only computes convolutions but also allows for a programmable forgetting rate, which in turn allows for a bio-inspired coincidence detection processing. Kernels are programmable and can be of arbitrary shape and arbitrary size of up to 32 32 pixels. The convolution processor operates on a pixel array of size 32 32, but can process an input space of up to 128 128 pixels. Larger pixel arrays can be directly processed by tiling arrays of chips. The chip receives and generates data in AER format, which is asynchronous and digital. However, its internal operation is based on analog low-current circuit techniques. The paper describes the architecture of the chip and circuits used for the pixels, including calibration techniques to overcome mismatch. Extensive experimental results are provided, describing pixel operation and calibration, convolution processing with and without forgetting, and high-speed recognition experiments like discriminating rotating propellers of different shape rotating at speeds of up to 5000 revolutions per second. ; Gobierno de España TIC2003-08164-C03-01 ; European Union IST-2001-34124

This paper describes CAVIAR, a massively parallel hardware implementation of a spike-based sensing-processing-learning-actuating system inspired by the physiology of the nervous system. CAVIAR uses the asychronous address-event representation (AER) communication framework and was developed in the context of a European Union funded project. It has four custom mixed-signal AER chips, five custom digital AER interface components, 45k neurons (spiking cells), up to 5M synapses, performs 12G synaptic operations per second, and achieves millisecond object recognition and tracking latencies. ; European Commission IST-2001-34124