34 research outputs found
AN EFFICIENT LOW-POWER CONTENT- ADDRESSABLE MEMORY USING COMPRESSOR MEMORY BLOCK
In this paper, we proposed a low-power content-addressable memory (CAM) employing a new algorithm for associativity between the input tag and the corresponding address of the output data. The proposed architecture is based on memory block. Given an input data the proposed architecture compares the stored data with input data and send the single matched data address as the output. Therefore, the dynamic energy consumption of the proposed design is significantly lower compared with that of a sparse Clustered network based CAM design. In this project we have shown as the effective error detection and correction in the data set. For detecting and correcting the data this project allows synergetic reuse COMPRESSOR MEMORY BLOCK. Â Â For very high speed searching applications, Bloom filters has been proposed. Associative memory, associative storage and associative array are the synonyms of CAM. For programming in data structures the name associative array is used most. XILINX ISE was used for the simulation process. The search delay of the proposed design is less. So the speed is more as compared to that of SCN CAM design
Content Addressable Memories and Transformable Logic Circuits Based on Ferroelectric Reconfigurable Transistors for In-Memory Computing
As a promising alternative to the Von Neumann architecture, in-memory
computing holds the promise of delivering high computing capacity while
consuming low power. Content addressable memory (CAM) can implement pattern
matching and distance measurement in memory with massive parallelism, making
them highly desirable for data-intensive applications. In this paper, we
propose and demonstrate a novel 1-transistor-per-bit CAM based on the
ferroelectric reconfigurable transistor. By exploiting the switchable polarity
of the ferroelectric reconfigurable transistor, XOR/XNOR-like matching
operation in CAM can be realized in a single transistor. By eliminating the
need for the complementary circuit, these non-volatile CAMs based on
reconfigurable transistors can offer a significant improvement in area and
energy efficiency compared to conventional CAMs. NAND- and NOR-arrays of CAMs
are also demonstrated, which enable multi-bit matching in a single reading
operation. In addition, the NOR array of CAM cells effectively measures the
Hamming distance between the input query and stored entries. Furthermore,
utilizing the switchable polarity of these ferroelectric Schottky barrier
transistors, we demonstrate reconfigurable logic gates with NAND/NOR dual
functions, whose input-output mapping can be transformed in real-time without
changing the layout. These reconfigurable circuits will serve as important
building blocks for high-density data-stream processors and reconfigurable
Application-Specific Integrated Circuits (r-ASICs). The CAMs and transformable
logic gates based on ferroelectric reconfigurable transistors will have broad
applications in data-intensive applications from image processing to machine
learning and artificial intelligence
Novel low power CAM architecture
One special type of memory use for high speed address lookup in router or cache address lookup in a processor is Content Addressable Memory (CAM). CAM can also be used in pattern recognition applications where a unique pattern needs to be determined if a match is found. CAM has an additional comparison circuit in each memory bit compared to Static Random Access Memory. This comparison circuit provides CAM with an additional capability for searching the entire memory in one clock cycle. With its hardware parallel comparison architecture, it makes CAM an ideal candidate for any high speed data lookup or for address processing applications. Because of its high power demand nature, CAM is not often used in a mobile device. To take advantage of CAM on portable devices, it is necessary to reduce its power consumption. It is for this reason that much research has been conducted on investigating different methods and techniques for reducing the overall power. The objective is to incorporate and utilize circuit and power reduction techniques in a new architecture to further reduce CAMâs energy consumption. The new CAM architecture illustrates the reduction of both dynamic and static power dissipation at 65nm sub-micron environment. This thesis will present a novel CAM architecture, which will reduce power consumption significantly compared to traditional CAM architecture, with minimal or no performance losses. Comparisons with other previously proposed architectures will be presented when implementing these designs under 65nm process environment. Results show the novel CAM architecture only consumes 4.021mW of power compared to the traditional CAM architecture of 12.538mW at 800MHz frequency and is more energy efficient over all other previously proposed designs
On Neural Associative Memory Structures: Storage and Retrieval of Sequences in a Chain of Tournaments
Associative memories enjoy many interesting properties in terms of error correction capabilities, robustness to noise, storage capacity, and retrieval performance, and their usage spans over a large set of applications. In this letter, we investigate and extend tournament-based neural networks, originally proposed by Jiang, Gripon, Berrou, and Rabbat (2016), a novel sequence storage associative memory architecture with high memory efficiency and accurate sequence retrieval. We propose a more general method for learning the sequences, which we call feedback tournament-based neural networks. The retrieval process is also extended to both directions: forward and backwardâin other words, any large-enough segment of a sequence can produce the whole sequence. Furthermore, two retrieval algorithms, cache-winner and explore-winner, are introduced to increase the retrieval performance. Through simulation results, we shed light on the strengths and weaknesses of each algorithm.publishedVersio
The sparse Blume-Emery-Griffiths model of associative memories
We analyze the Blume-Emery-Griffiths (BEG) associative memory with sparse
patterns and at zero temperature. We give bounds on its storage capacity
provided that we want the stored patterns to be fixed points of the retrieval
dynamics. We compare our results to that of other models of sparse neural
networks and show that the BEG model has a superior performance compared to
them.Comment: 23 p