Data compressing, data coding, and communications in object-oriented multimedia applications like telepresence, computer-aided medical diagnosis, or telesurgery require an enormous computing power - in the order of trillions of operations per second (TeraOPS). Compared with conventional digital technology, cellular neural/nonlinear network (CNN)-based computing is capable of realizing these TeraOPS-range image processing tasks in a cost-effective implementation. To exploit the computing power of the CNN Universal Machine (CNN-UM), the CNN chipset architecture has been developed a mixed-signal hardware platform for CNN-based image processing. One of the nonstandard components of the chipset is the cache memory of the analog array processor, the analog random access memory (ARAM). This paper reports on an ARAM chip that has been designed and fabricated in a 0.5-μm CMOS technology. This chip consists of a fully addressable array of 32×256 analog memory registers and has a packing density of 637 analog-memory-cells/mm2. Random and nondestructive access of the memory contents is available. Bottom-plate sampling techniques have been employed to eliminate harmonic distortion introduced by signal-dependent feedthrough. Signal coupling and interaction have been minimized by proper layout measures, including the use of protection rings and separate power supplies for the analog and the digital circuitry. This prototype features an equivalent resolution of up to 7 bits-measured by comparing the reconstructed waveform with the original input signal. Measured access times for writing/reading to/from the memory registers are of 200 ns. I/O rates via the l6-line-wide I/O bus exceed 10 Msamples/s. Storage time at room temperature is in the 80 to 100 ms range, without accuracy loss

Comisión Interministerial de Ciencia y Tecnología TIC96-1392-C0202 SIVA

Office of Naval Research N00014-98-1-0052

European Union ESPRIT IV 27077-DICTAM

JSEP FDF49620-97-1-0220-03/98