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Content-addressable memory (CAM) is a special type of computer memory used in certain very-high-speed searching applications. It is also known as associative memory or associative storage and compares input search data against a table of stored data, and returns the address of matching data. [1]
A Hopfield network (or associative memory) is a form of recurrent neural network, or a spin glass system, that can serve as a content-addressable memory. The Hopfield network, named for John Hopfield , consists of a single layer of neurons, where each neuron is connected to every other neuron except itself.
A tuple space is an implementation of the associative memory paradigm for parallel/distributed computing. It provides a repository of tuples that can be accessed concurrently. As an illustrative example, consider that there are a group of processors that produce pieces of data and a group of processors that use the data.
In psychology, associative memory is defined as the ability to learn and remember the relationship between unrelated items. This would include, for example, remembering the name of someone or the aroma of a particular perfume. [1] This type of memory deals specifically with the relationship between these different objects or concepts.
Set-associative cache is a trade-off between direct-mapped cache and fully associative cache. A set-associative cache can be imagined as a n × m matrix. The cache is divided into ‘n’ sets and each set contains ‘m’ cache lines. A memory block is first mapped onto a set and then placed into any cache line of the set.
These rules usually take two variables as input, mapping cleanly to a two-dimensional matrix, although theoretically a matrix of any number of dimensions is possible. From the perspective of neuro-fuzzy systems, the mathematical matrix is called a "Fuzzy associative memory" because it stores the weights of the perceptron. [1]
Autoassociative memory, also known as auto-association memory or an autoassociation network, is any type of memory that is able to retrieve a piece of data from only a tiny sample of itself. They are very effective in de-noising or removing interference from the input and can be used to determine whether the given input is “known” or ...
The memory or storage capacity of BAM may be given as (,), where "" is the number of units in the X layer and "" is the number of units in the Y layer. [3]The internal matrix has n x p independent degrees of freedom, where n is the dimension of the first vector (6 in this example) and p is the dimension of the second vector (4).