High Performance Associative Memory: Practical Generalizations of the Hamming Net

高性能联想记忆：汉明网络的实用推广

• 批准号: 9618597
• 负责人: Mohamad Hassoun
• 金额: $ 30.1万
• 依托单位: Wayne State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-15 至 2001-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9618597&HistoricalAwards=false
• 关键词: Performance; Associative; Memory; Practical; Generalizations

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.

设计高性能的联想记忆一直是人工神经网络研究者的目标。 一种实用的低成本设备，可以存储和检索类似于人类的“内容可寻址”存储器的信息，将是识别，数据库，控制系统和机器人。 事实上，大多数专家认为，联想记忆是开发任何真正智能和类脑系统的关键组成部分。 本计画的目的是设计并实作一个实用且低成本的联想记忆体，其特性如下：1。高容量。 2.大量的错误校正和容忍噪声或部分输入。 3. 没有寄生记忆和极限环。 4. “无决定”或基态的规定。 5.快速记忆恢复。 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。 该方法将推广的汉明存储器的操作，以设计联想记忆模型，保留所有的高性能的汉明网络的属性，但允许一个更实际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个扩展：（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，（iv）细胞汉明存储器。