The protein processor associative memory on a robotic hand-eye coordination task

The PPAM is a hardware architecture for a robust, bidirectional and scalable hetero-associative memory. It is fundamentally different from the traditional processing methods which use arithmetic operations and consequently ALUs. In this paper, we present the results of applying the PPAM to a real-world robotics hand-eye coordination task. A comparison is performed with a nearest neighbour technique that was originally used to associate the same dataset. The number of memory load/store operations and the number of ALU operations for the nearest neighbour algorithm is compared with the corresponding PPAM which acheives the same association. It was determined that 29 conflict resolving nodes were required to fully store and recall the entire dataset and the maximum number of memory locations required in any node was 160, with the average and quartiles being much lower.