Contents-addressable memory (CAM) is a special that searches the input data with entire pre-loaded database and generates corresponding address information. CAMs are advancing to be core technology in computer networking systems. As field programmable gate array (FPGA) recently being used for network acceleration applications, demand integrate CAM on FPGA increasing. FPGA-based divided into thr...

This paper proposes a novel Hamming distance tolerant content-addressable memory (HD-CAM) for energy-efficient in-memory approximate matching applications. HD-CAM exploits NOR-type based static associative bitcells, where we add circuitry to enable search with programmable tolerance. implements using matchline charge redistribution rather than its rise or fall time, frequently employed in state...

Combining neuropharmacological experiments with computational modeling, we have shown that cholinergic modulation may enhance associative memory function in piriform (olfactory) cortex. We have shown that the acetylcholine analogue carbachol selectively suppresses synaptic transmission between cells within piriform cortex, while leaving input connections unaffected. When tested in a computation...

The present many-to-many associative memory models can not carry out the multi-granularity association. In this paper some many-to-many multi-granularity associative memory models based on incidence of patterns are proposed by use of introducing logic calculation network and granularity control network. The model can control the granularity of output and carry out multi-granularity association.

We present a linguistic extension from a crisp model by using a codification model that allows us to implement a fuzzy system on a discrete decision model. The paper begins with an introduction to the representation of fuzzy information, followed by a discussion of the codification method and the extension of a linear associative memory to a linguistic linear associative memory. Finally, we enu...

Associative memory in cortical circuits has been held as a major mechanism for content-addressable memory. Hebbian synapses implement associative memory efficiently when storing sparse binary activity patterns. However, in models of sensory processing, representations are graded and not binary. Thus, it has been an unresolved question how sensory computation could exploit cortical associative m...

We combine statistical-mechanical approach for probabilistic image processing with DNA based biomolecular operations to construct associative memory for pattern recall. The statistical properties of the patterns are learned from the exposed examples, stored in memory and are recalled when presented with partial queries. The results show that our proposed memory model retrieves patterns with hig...