We propose a broad-based target classifier that recognizes a target on the principle of pattern matching based on associative memory, and can be implemented in hardware with standard complementary metal-oxide semiconductor (CMOS) cells. A majordrawback in the field of neural networks is the inability to implement network designs in hardware inexpensively. Here we have designed an associative RAM-net memory neural classifier, which is based on the associative memory model of a winner-take-allclassifier. In this model extensive use of ordinary random access memory is made. The benefit of this approach is that the entire architecture can be designed with current CMOS standard cells application-specific integrated circuit (ASIC) technologies asopposed to current analog very large scale integration (VLSI) approaches. Since RAM can be easily added using the standard cell ASIC approach, a low-cost implementation for a wide variety of neural classification problems is provided. The integration ofall necessary features on a single chip results in an easily implementable, low-chip-count classification system. A full gate-level design of the architecture is created and simulated using the VHDL hardware programming language (Navabi, 1993; MentorGraphics, 1992). The results are very encouraging, and warrant further research into various memory types and configurations.
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