2,616 research outputs found
Rutger's CAM2000 chip architecture
This report describes the architecture and instruction set of the Rutgers CAM2000 memory chip. The CAM2000 combines features of Associative Processing (AP), Content Addressable Memory (CAM), and Dynamic Random Access Memory (DRAM) in a single chip package that is not only DRAM compatible but capable of applying simple massively parallel operations to memory. This document reflects the current status of the CAM2000 architecture and is continually updated to reflect the current state of the architecture and instruction set
Probabilistic Quantum Memories
Typical address-oriented computer memories cannot recognize incomplete or
noisy information. Associative (content-addressable) memories solve this
problem but suffer from severe capacity shortages. I propose a model of a
quantum memory that solves both problems. The storage capacity is exponential
in the number of qbits and thus optimal. The retrieval mechanism for incomplete
or noisy inputs is probabilistic, with postselection of the measurement result.
The output is determined by a probability distribution on the memory which is
peaked around the stored patterns closest in Hamming distance to the input.Comment: Revised version to appear in Phys. Rev. Let
Optical implementation of the Hopfield model
Optical implementation of content addressable associative memory based on the Hopfield model for neural networks and on the addition of nonlinear iterative feedback to a vector-matrix multiplier is described. Numerical and experimental results presented show that the approach is capable of introducing accuracy and robustness to optical processing while maintaining the traditional advantages of optics, namely, parallelism and massive interconnection capability. Moreover a potentially useful link between neural processing and optics that can be of interest in pattern recognition and machine vision is established
Capacity, Fidelity, and Noise Tolerance of Associative Spatial-Temporal Memories Based on Memristive Neuromorphic Network
We have calculated the key characteristics of associative
(content-addressable) spatial-temporal memories based on neuromorphic networks
with restricted connectivity - "CrossNets". Such networks may be naturally
implemented in nanoelectronic hardware using hybrid CMOS/memristor circuits,
which may feature extremely high energy efficiency, approaching that of
biological cortical circuits, at much higher operation speed. Our numerical
simulations, in some cases confirmed by analytical calculations, have shown
that the characteristics depend substantially on the method of information
recording into the memory. Of the four methods we have explored, two look
especially promising - one based on the quadratic programming, and the other
one being a specific discrete version of the gradient descent. The latter
method provides a slightly lower memory capacity (at the same fidelity) then
the former one, but it allows local recording, which may be more readily
implemented in nanoelectronic hardware. Most importantly, at the synchronous
retrieval, both methods provide a capacity higher than that of the well-known
Ternary Content-Addressable Memories with the same number of nonvolatile memory
cells (e.g., memristors), though the input noise immunity of the CrossNet
memories is somewhat lower
Study of Virtual Memory
This research report gives a general description of virtual memory systems. The mechanisms and policies and their effect on the operation and efficiency of virtual memory are explained. A virtual memory using a real time virtual address decoder, to decode a 32 bits of virtual address for the secondary memory to obtain the primary address location discussed. The decoder is developed with the use of associative or content-addressable memories. Replacement algorithms, used for selecting the pages of the main memory to be replaced, are described. The hardware implementation of the least recently used and least often used replacement policies using associative memories is presented
Large-scale memristive associative memories
Associative memories, in contrast to conventional address-based memories, are inherently fault-tolerant and allow retrieval of data based on partial search information. This paper considers the possibility of implementing large-scale associative memories through memristive devices jointly with CMOS circuitry. An advantage of a memristive associative memory is that the memory elements are located physically above the CMOS layer, which yields more die area for the processing elements realized in CMOS. This allows for high-capacity memories even while using an older CMOS technology, as the capacity of the memory depends more on the feature size of the memristive crossbar than on that of the CMOS components. In this paper, we propose the memristive implementations, and present simulations and error analysis of the autoassociative content-addressable memory, the Willshaw memory, and the sparse distributed memory. Furthermore, we present a CMOS cell that can be used to implement the proposed memory architectures.</div
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