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。识别、数据库、控制系统和机器人是一种实用的低成本设备，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，大多数专家认为，联想记忆是任何真正智能和类脑系统发展的关键组成部分。本课题的目的是设计并实现一种实用的、低成本的联想存储器，它具有以下特性：高容量。2. 高纠错量和对噪声或部分输入的容忍度。3. 没有虚假的记忆，没有极限环。4. 提供“无决定”或基态。5. 快速记忆检索。6. 能够在自联想或异联想模式下操作。7. 实用和低成本的硬件实现。该方法将概括汉明记忆的操作，以设计保留汉明网络所有高性能特性的联想记忆模型，但允许更实用的硬件实现和更快的检索。特别是，该项目将探索汉明存储器的四种扩展：(i)接地汉明存储器，（ii）解耦汉明存储器，（ii）两级解耦汉明存储器，以及（iv）细胞汉明存储器。

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.