Image processing using one-dimensional processor arrays

The first half of this paper presents the design rationale for CNAPS, a specialized one-dimensional (1-D) processor array developed by Adaptive Solutions Inc. In this context, we discuss the problem of Amdahl's law which severely constrains special-purpose architectures. We also discuss specific architectural decisions such as the kind of parallelism, the computational precision of the processors, on-chip versus off-chip processor memory, and-most importantly-the interprocessor communication architecture. We argue that, for our particular set of applications, a 1-D architecture gives the best "bang for the buck", even when compared to the more traditional two-dimensional (2-D) architecture. The second half of this paper describes how several simple algorithms map to the CNAPS array. Our results show that the CNAPS 1-D array offers excellent performance over a range of IP algorithms. We also briefly look at the performance of CNAPS as a pattern recognition engine because many image processing and pattern recognition problems are intimately related.