Second-order neural core for bioinspired focal-plane dynamic image processing in CMOS

Based on studies of the mammalian retina, a bioinspired model for mixed-signal array processing has been implemented on silicon. This model mimics the way in which images are processed at the front-end of natural visual pathways, by means of programmable complex spatio-temporal dynamic. When embedded into a focal-plane processing chip, such a model allows for online parallel filtering of the captured image; the outcome of such processing can be used to develop control feedback actions to adapt the response of photoreceptors to local image features. Beyond simple resistive grid filtering, it is possible to program other spatio-temporal processing operators into the model core, such as nonlinear and anisotropic diffusion, among others. This paper presents analog and mixed-signal very large-scale integration building blocks to implement this model, and illustrates their operation through experimental results taken from a prototype chip fabricated in a 0.5-μm CMOS technology.European Union IST 2001 38097Ministerio de Ciencia y Tecnología TIC 2003 09817 C02 01Office of Naval Research (USA) N00014021088

A Bio-Inspired Two-Layer Mixed-Signal Flexible Programmable Chip for Early Vision

A bio-inspired model for an analog programmable array processor (APAP), based on studies on the vertebrate retina, has permitted the realization of complex programmable spatio-temporal dynamics in VLSI. This model mimics the way in which images are processed in the visual pathway, what renders a feasible alternative for the implementation of early vision tasks in standard technologies. A prototype chip has been designed and fabricated in 0.5 μm CMOS. It renders a computing power per silicon area and power consumption that is amongst the highest reported for a single chip. The details of the bio-inspired network model, the analog building block design challenges and trade-offs and some functional tests results are presented in this paper.Office of Naval Research (USA) N-000140210884European Commission IST-1999-19007Ministerio de Ciencia y Tecnología TIC1999-082

Exploration of spatial-temporal dynamic phenomena in a 32×32-cell stored program two-layer CNN universal machine chip prototype

This paper describes a full-custom mixed-signal chip that embeds digitally programmable analog parallel processing and distributed image memory on a common silicon substrate. The chip was designed and fabricated in a standard 0.5 μm CMOS technology and contains approximately 500 000 transistors. It consists of 1024 processing units arranged into a 32 × 32 grid. Each processing element contains two coupled CNN cores, thus, constituting two parallel layers of 32 × 32 nodes. The functional features of the chip are in accordance with the 2nd Order Complex Cell CNN-UM architecture. It is composed of two CNN layers with programmable inter- and intra-layer connections between cells. Other features are: cellular, spatial-invariant array architecture; randomly selectable memory of instructions; random storage and retrieval of intermediate images. The chip is capable of completing algorithmic image processing tasks controlled by the user-selected stored instructions. The internal analog circuitry is designed to operate with 7-bits equivalent accuracy. The physical implementation of a CNN containing second order cells allows real-time experiments of complex dynamics and active wave phenomena. Such well-known phenomena from the reaction-diffusion equations are traveling waves, autowaves, and spiral-waves. All of these active waves are demonstrated on-chip. Moreover this chip was specifically designed to be suitable for the computation of biologically inspired retina models. These computational experiments have been carried out in a developmental environment designed for testing and programming the analogic (analog-and-logic) programmable array processors.Hungarian Academy of Sciences SIVA-2Comisión Interministerial de Ciencia y Tecnología TICC99-0826Office of Naval Research (USA) N00014-00-1-042

Behavior and event detection for annotation and surveillance

Visual surveillance and activity analysis is an active research field of computer vision. As a result, there are several different algorithms produced for this purpose. To obtain more robust systems it is desirable to integrate the different algorithms. To achieve this goal, the paper presents results in automatic event detection in surveillance videos, and a distributed application framework for supporting these methods. Results in motion analysis for static and moving cameras, automatic fight detection, shadow segmentation, discovery of unusual motion patterns, indexing and retrieval will be presented. These applications perform real time, and are suitable for real life applications

Receptive field atlas and related CNN models

In this paper we demonstrate the potential of the cellular nonlinear/neural network paradigm (CNN) that of the analogic cellular computer architecture (called CNN Universal Machine | CNN-UM) in modeling different parts and aspects of the nervous system. The structure of the living sensory systems and the CNN share a lot of features in common: local interconnections ("receptive field architecture"), nonlinear and delayed synapses for the processing tasks, the potentiality of feedback and using the advantages of both the analog and logic signal-processing mode. The results of more than ten years of cooperative work of many engineers and neurobiologists have been collected in an atlas: what we present here is a kind of selection from these studies emphasizing the exibility of the CNN computing: visual, tactile and auditory modalities are concerned