Adapting constant multipliers in a neural network implementation

The use of dynamic reconfiguration appears extremely attractive for implementing adaptive processing algorithms. Often, the adaption involves updating look-up tables based on a parameter which can only be determined at run-time. For reasons of efficiency, these look-up tables are read-only to the rest of the circuitry. This paper compares the use of run-time reconfiguration and read-only look-up tables, with a similar implementation using writable memories. The application under consideration is the multilayer perceptron neural network