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Gate diffusion input (Gdi) technique based CAM cell design for low power and high performance
Published in Arun Kumar Sinha, John Pradeep Darsy, Computer-Aided Developments: Electronics and Communication, 2019
S.V.V. Satyanarayana, Sridevi Sriadibhatla, Nannuru Amarnath
Content addressable memory (CAM) is a particular type of associative memory used in high speed parallel search and compare applications. CAM performs read and write operation as conventional memory, besides it also performs the comparison operation. CAM is the best alternative to random access memory (RAM) for searching the data with high speed, but it degrades from cost, area, and power consumption point of view. The relation between the CAM approach and RAM approach of memory operations is inversely proportional to each other. RAM operation depends on the control signal and address of memory data to write or retrieve the data. Whereas the CAM operation depends on the search input content, and the content of memory data to write and search the data. CAM performs writing as well as searching of the data within a single cycle. CAM can be used in wide variety of low power and high-performance applications such as image processing [1], gray-coding [2], parametric-curve extraction [3], 5G communication network [4], IP routing [5], etc.
Memory Devices
Published in Jerry C. Whitaker, Microelectronics, 2018
A special type of memory called content addressable memory (CAM) or associative memory is used in many applications such as cache memory and associative processor. A CAM stores a data item consisting of a tag and a value. Instead of giving an address, a data pattern is given to the tag section of the CAM. This data pattern is matched with the content of the tag section. If an item in the tag section of the CAM matches the supplied data pattern, the CAM outputs the value associated with the matched tag. Figure 8.17 illustrates the basic structure of a CAM. CAM cells must be both readable and writable just like the RAM cell. Figure 8.18 shows a circuit diagram for a basic CAM cell with a match output signal. This output signal may be used as an input for some logic to determine the matching process.
Shallow Neural Networks
Published in Adrian A. Hopgood, Intelligent Systems for Engineers and Scientists, 2021
A content-addressable memory (CAM) is a type of computer memory that can rapidly find stored content. A CAM model can be achieved in a neural network using a form of supervised learning. During training, each example input vector becomes stored in a dispersed form through the network. There are no separate desired output vectors associated with the training data, as the training data represent both the inputs and the desired outputs.
Stability of three-dimensional icosahedral quasicrystals in multi-component systems
Published in Philosophical Magazine, 2020
Besides a proper free energy functional to describe multi-component systems, examining the thermodynamic stability of quasicrystals requires accurate and efficient methods to evaluate the free energy of various ordered phases. Due to the lack of translational symmetry, the computation of quasicrystals is harder to carry out within a finite domain as done for periodic crystals. In the literature, utilising a large periodic structure to approximate a quasicrystal is a commonly used method in the study of the quasicrystalline order [18,19,27–29]. The method actually obtains a crystalline approximant, therefore, it is named the crystalline approximant method (CAM). From the viewpoint of numerical computation, CAM is based on the approximation of irrational numbers by integers or rational numbers, corresponding to well-known Diophantine approximation (DA) problem in the number theory [2,30]. Because of the existence of DA, CAM has to be implemented in a very large computational region which means an unacceptable computational amount if small DA error is required. Furthermore, it has been verified that the gap between the free energy of quasicrystals and their corresponding approximants cannot be vanished in any finite computational region [2]. In order to avoid the approximation error, an alternative approach is proposed to calculate quasicrystals based on the fact that quasiperiodic lattices can be generated by a cut-and-project method from higher-dimensional periodic lattices [2]. This method provides a basic framework to study quasicrystals, originally proposed by Meyer in studying the relationship between harmonic analysis and algebraic numbers [2]. More recently, Jiang and Zhang proposed a projection method (PM) to obtain the density profile of quasicrystals and evaluate their energy density on high accuracy. The PM shows that the Fourier spectrum of a quasiperiodic structure can be embedded into a higher-dimensional crystallographic point packing set of corresponding periodic structure. Consequently, quasicrystals can be computed precisely in a higher-dimensional space and then be recovered by projecting the higher-dimensional reciprocal lattice vectors back to the original Fourier space through a projection matrix. This method can avoid the DA error effectively. As a particular case, it can be further used to investigate periodic crystals by setting the projection matrix as an identity matrix. From this perspective, the PM can calculate free energy of quasicrystals and periodic crystals with the same accuracy. Therefore, the PM provides a unified computational framework to study the relative stability of quasicrystals and periodic crystals.