A DTCNN UNIVERSAL MACHINE BASED ON HIGHLY PARALLEL 2-D CELLULAR-AUTOMATA CAM(2)

The discrete-time cellular neural network (DTCNN) is a promising compu ter paradigm that fuses artificial neural networks with the concept of cellular automaton (CA) and has many applications to pixel-level imag e processing, Although some architectures have been proposed for proce ssing DTCNN, there are no compact, practical computers that can proces s real-world images of several hundred thousand pixels at video rates. So, in spite of its great potential, DTCNN's are not being used for i mage processing outside the laboratory, This paper proposes a DTCNN pr ocessing method based on a highly parallel two-dimensional (2-D) cellu lar automata called CAM(2). CAM(2) can attain pixel-order parallelism on a single PC board because it is composed of a content addressable m emory (CAM), which makes it possible to embed enormous numbers of proc essing elements, corresponding to CA cells, onto one VLSI chip, A new mapping method utilizes maskable search and parallel and partial write commands of CAM(2) to enable high-performance DTCNN processing. Evalu ation results show that, on average, CAM(2) can perform one transition for various DTCNN templates in about 12 microseconds, And it cam perf orm practical image processing through a combination of DTCNN's and ot her CA-based algorithms. CAM(2) is a promising platform for processing DTCNN.