PPKE ITK PhD and MPhil Thesis Classes

Chapter 4
Implementing a Global Analogic
Programming Unit for Emulated
Digital CNN Processors on
FPGA
4.1 Introduction
Cellular Neural/Nonlinear Networks (CNN) are defined as locally connected, analog,
stored programmable processor arrays [68], visual microprocessors. The topographic,
sensory, Cellular Wave Computer architectures, based on the CNN-
UM (Universal Machine) principle, have been implemented in various physical
forms [69] such as mixed-mode CMOS VLSI, emulated-digital (both on ASIC
and FPGA), DSP, and optical implementations.
The analog VLSI implementation of the extended CNN-UM architectures
([22], [70], [23], [25], [71], and [24]) exhibit very high speed (few TeraOP/s) with
low power dissipation, but these architectures have some disadvantages: they
are known to have relative low accuracy (about 7-8 bit) and moderate flexibility,
moreover the number of cells is limited (e.g., 128 × 128 on ACE16k, or 176 × 144
on eye-RIS). On the one hand their cost is high, and the development time is
long, due to the utilization of full-custom VLSI technology. On the other hand,
the highly flexible software solutions (running on host processors or DSPs) are
usually insufficient, considering the performance of computations.
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