High Performance Associative Memory: Practical Generalizations of the Hamming Net

高性能联想记忆的设计一直是人工神经网络研究人员追求的目标。一种实用的低成本设备将是识别、数据库、控制系统和机器人，它可以存储和检索类似于人类“内容可寻址”记忆的信息。事实上，联想记忆被大多数专家视为开发任何真正智能和类似大脑的系统的关键组成部分。本课题的目的是设计并实现一种实用、低成本的联想存储器，具有以下特性：1.大容量。2.高纠错量和对噪声或部分输入的容忍度。3.没有虚假记忆，没有极限环。4.关于“不作决定”或“基态”的规定。5.快速内存检索。6.能够在自关联或异关联模式下操作。7.实用、低成本的硬件实现。该方法将推广汉明记忆的操作，以便设计关联记忆模型，该模型保留了汉明网络的所有高性能属性，但允许更实用的硬件实现和更快的检索。特别是，该项目将探索Hamming记忆的四个扩展：(I)接地的Hamming记忆，(Ii)去耦合的Hamming记忆，(Ii)两级去耦合的Hamming记忆，和(Iv)细胞Hamming记忆。

The design of high performance associative memory has long been a goal of artificial neural net researchers. A practical low-cost device which could store and retrieve information similar to the human `content addressable` memory would be recognition, data base, control systems, and robotics. Indeed, associative memory is seen by most experts as a crucial component for the development of any truly intelligent and brain-like system. The purpose of this project is to design and implement a practical and low cost associative memory with following properties: 1. High capacity. 2. High amount of error correction and tolerance to noisy or partial inputs. 3. No spurious memories and no limit cycles. 4. Provision for a `no decision` or ground state. 5. Fast memory retrievals. 6. Ability to operate in an autoassociative or heteroassociative mode. 7. Practical and low cost hardware implementation. The approach will be to generalize the operation of the Hamming memory in order to design associative memory models which retain all the high performance properties of the Hamming net, but allow for a much more practical hardware implementation and faster retrievals. In particular the project will explore four extensions of the Hamming memory: (i) the Grounded Hamming Memory, (ii) the Decoupled Hamming Memory, (ii) Two Level Decoupled Hamming Memory, and (iv) the Cellular Hamming Memory.