A continuous-time cellular neural network chip for direction-selectable connected component detection with optical image acquisition

This paper presents a continuous-time Cellular Neural Network (CNN) chip for the application of Connected Component Detection (CCDet). Projection direction can be selected among four different possibilities. Every cell (or pixel) in the 32×32 array includes a photosensor circuitry and an automatic tuning circuitry to adapt to average environmental illumination. Electrical image uploading is possible as well. Input pixel-values are stored on local memories (one per cell), allowing sequential processing of the acquired image in different directions. The prototype has been designed and fabricated on a standard digital CMOS technology: 1.6 μm, n-well, single-poly, double-metal. Circuit implementation is based on current-mode techniques and uses a systematic approach valid for any CNN application. Cell dimensions, including the CNN processing circuitry, the photosensor and the adaptive circuitry are 145×150 μm², of which the sensor and adaptive circuitry amounts to ~15% of the total pixel area and the wiring and multiplexing (required for direction selectability) to about 40%. The remaining 45% corresponds to the CNN processing circuitry. Pixel density is ~46 cell/mm², and power dissipation is 0.33 mW/cell. These area and power figures forecast single-die CMOS chips with 100×100 complexity and about 3 W power consumption