Architecture and Design of 1-D Enhanced Cellular NeuralNetwork Processors for Signal Detection

Abstract

One-dimensional cellular array processor architecture and design for neural-based partial response (PR) signal detection are presented. Analog parallel computing approaches have many attractive advantages in achieving low power, low cost, and faster processing speed by its uniquely coupled parallel and distributed processing nature. In this paper, we describe the maximum likelihood sequence estimation (MLSE) algorithm for PR signals, the enhanced Cellular Neural Network (CNN) processor array architecture to realize the detection algorithm, and system performance evaluation. Analytical models and simulations on a design example of the detector have been employed to demonstrate the advantages of this scalable VLSI architecture. A processing rate of 265 Mbps was achieved for a prototype detector on a silicon area of 5.14 mm by 5.81 mm is a 1.2 µm CMOS technology. The processing rate can be beyond 1Gbps if it is implemented in the same amount of silicon area by using 0.5 µm CMOS technology. Such promising results clearly demonstrate the ability to meet the needs in future high speed data communication by VLSI realization of maximum likelihood sequence detectors based on the enhanced cellular neural network paradigm.