Efficient Human-like Memory Management based on Walsh-based Associative Memory for Real-time Pattern Recognition

This paper proposes an efficient human-like memory and memory management which utilizes Walsh-based distributed associative memory in reducing the computer storage and processing for pattern recognition. As a verification example, a memory storing 26 binary alphabet images takes only the physical space needed to store 8 patterns and yet capable of perfect recognition. Further, the experimental results show that the proposed memory management strategy can nicely deal with data transfer from short-term (working) memory to long-term memory.