DESIGN OF A NEUROMORPHIC EDGE DETECTOR VISION CHIP WITH COLOR AND INTENSITY CHANGE DISAMBIGUATION ABSTRACT A neuromorphic edge detector with color and intensity change disambiguation using only one photo detector per pixel has been developed. The proposed structure is very simple, compact, and low power. It dissipates about 100nW per pixel with 1V power supply. It can handle a wide range of input light luminosity and process image streams up to about 30 frames per second. The detector has been designed and simulated in TSMC 0.18um CMOS process. Index Terms—mage edge detection, Image color analysis, Real-time image processing, Analog processing circuits, Integrated circuits 1. INTRODUCTION Real-time Edge detection is a fundamental tool in image processing and machine vision. It is used as a preprocessing step in many image understanding applications. The traditionally frame-based approach to deal with vision tasks is based on two following main steps: 1) acquisition of the image data by a low-cost CCD camera and 2) software processing of the data on a digital platform (DSP or PC) [1]. The frame-based approach carries some disadvantages due to its relatively high power dissipation and high processor cost. An attractive solution to this problem is to shift part of the image data processing into the sensor hardware structure. It is done by designing and allocating some circuitry around photo- captor to perform some early pixel-level image processing in the photo-sensor array [1]. In this way, the CCD camera (or CMOS imager) is replaced with a more sophisticated device which is able to preprocess the acquired image data, and hence to provide the next processing stages with less requirements in terms of computational power, cost, portability and power consummation [2]. One area of research is the use of neuromorphic engineering techniques to implement processing circuits inspired by biological neural systems. In the present work, in-pixel processing circuitry of a real-time neuromorphic edge detector with color and density change disambiguation is introduced. The proposed detector has been designed on the basis of retina models in color and edge detection. Buried Triple Junction photo detector (BTJ) is used as the pixel light sensing element. In the following, in section 2 retina structure is explained. in Section 3, the proposed edge detector system and its general basic blocks are explained. Section 4 describes in more detail the color and intensity change detection blocks. Section 5 presents the simulation results, and conclusion is provided in Section 6. 2. BIOLOGICAL INPIRATION The retina is the first neural structure to be used in biological visual data processing. It filters input signals and extracts only the relevant information necessary in the visual cortex for high level visual processing tasks[3]. Fig. 1 shows schematic cross section of the Macaque retina containing the basic neural cells. The photoreceptors convert incident light into electrical signals. Each horizontal cell (H) spreads information over neighboring horizontal cells and thus performs spatiotemporal low-pass filtering on the photoreceptor outputs. Bipolar cells (B) deliver the difference between the receptor and horizontal cell responses. The difference signal then will be encoded and transmitted through the optical nerve and edges will be extracted from this information. Trichromatic color vision is the ability of humans and some other animals to see different colors by possessing three independent channels for conveying color information, derived from the three different cone types. The three types of cones are L, M, and S, which have pigments that respond best to light of long (especially 560 nm), medium (530 nm), and short (420 nm) wavelengths respectively[4][5]. Fig. 1: Schematic cross section of the Macaque retina [3] Hesam Ahmadi*, Sayed Masoud Sayedi** *ECE, Isfahan University Of Technology, Isfahan 84156-83111, Iran, h.ahmadi@ec.iut.ac.ir ** ECE, Isfahan University Of Technology, Isfahan 84156-83111, Iran, m_sayedi@cc.iut.ac.ir 2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) 978-1-4673-1433-6/12/$31.00 ©2012 IEEE