2,678 research outputs found
Memristor MOS Content Addressable Memory (MCAM): Hybrid Architecture for Future High Performance Search Engines
Large-capacity Content Addressable Memory (CAM) is a key element in a wide
variety of applications. The inevitable complexities of scaling MOS transistors
introduce a major challenge in the realization of such systems. Convergence of
disparate technologies, which are compatible with CMOS processing, may allow
extension of Moore's Law for a few more years. This paper provides a new
approach towards the design and modeling of Memristor (Memory resistor) based
Content Addressable Memory (MCAM) using a combination of memristor MOS devices
to form the core of a memory/compare logic cell that forms the building block
of the CAM architecture. The non-volatile characteristic and the nanoscale
geometry together with compatibility of the memristor with CMOS processing
technology increases the packing density, provides for new approaches towards
power management through disabling CAM blocks without loss of stored data,
reduces power dissipation, and has scope for speed improvement as the
technology matures.Comment: 10 pages, 11 figure
Distributed ARTMAP
Distributed coding at the hidden layer of a multi-layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically requires slow off-line learning to avoid catastrophic forgetting in an open input environment. An adaptive resonance theory (ART) model is designed to guarantee stable memories even with fast on-line learning. However, ART stability typically requires winner-take-all coding, which may cause category proliferation in a noisy input environment. Distributed ARTMAP (dARTMAP) seeks to combine the computational advantages of MLP and ART systems in a real-time neural network for supervised learning. This system incorporates elements of the unsupervised dART model as well as new features, including a content-addressable memory (CAM) rule. Simulations show that dARTMAP retains fuzzy ARTMAP accuracy while significantly improving memory compression. The model's computational learning rules correspond to paradoxical cortical data.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657
Bionanomaterials from plant viruses
Plant virus capsids have emerged as useful biotemplates for material synthesis. All plant virus capsids are assembled with high-precision, three-dimensional structures providing nanoscale architectures that are highly monodisperse, can be produced in large quantities and that cannot replicate in mammalian cells (so are safe). Such exceptional characteristics make plant viruses strong candidates for application as biotemplates for novel and new material synthesis
Analog Content-Addressable Memory from Complementary FeFETs
To address the increasing computational demands of artificial intelligence
(AI) and big data, compute-in-memory (CIM) integrates memory and processing
units into the same physical location, reducing the time and energy overhead of
the system. Despite advancements in non-volatile memory (NVM) for matrix
multiplication, other critical data-intensive operations, like parallel search,
have been overlooked. Current parallel search architectures, namely
content-addressable memory (CAM), often use binary, which restricts density and
functionality. We present an analog CAM (ACAM) cell, built on two complementary
ferroelectric field-effect transistors (FeFETs), that performs parallel search
in the analog domain with over 40 distinct match windows. We then deploy it to
calculate similarity between vectors, a building block in the following two
machine learning problems. ACAM outperforms ternary CAM (TCAM) when applied to
similarity search for few-shot learning on the Omniglot dataset, yielding
projected simulation results with improved inference accuracy by 5%, 3x denser
memory architecture, and more than 100x faster speed compared to central
processing unit (CPU) and graphics processing unit (GPU) per similarity search
on scaled CMOS nodes. We also demonstrate 1-step inference on a kernel
regression model by combining non-linear kernel computation and matrix
multiplication in ACAM, with simulation estimates indicating 1,000x faster
inference than CPU and GPU
An Energy-Efficient Design Paradigm for a Memory Cell Based on Novel Nanoelectromechanical Switches
In this chapter, we explain NEMsCAM cell, a new content-addressable memory (CAM) cell, which is designed based on both CMOS technologies and nanoelectromechanical (NEM) switches. The memory part of NEMsCAM is designed with two complementary nonvolatile NEM switches and located on top of the CMOS-based comparison component. As a use case, we evaluate first-level instruction and data translation lookaside buffers (TLBs) with 16 nm CMOS technology at 2 GHz. The simulation results demonstrate that the NEMsCAM TLB reduces the energy consumption per search operation (by 27%), standby mode (by 53.9%), write operation (by 41.9%), and the area (by 40.5%) compared to a CMOS-only TLB with minimal performance overhead
Quantum-dot Cellular Automata: Review Paper
Quantum-dot Cellular Automata (QCA) is one of the most important discoveries that will be the successful alternative for CMOS technology in the near future. An important feature of this technique, which has attracted the attention of many researchers, is that it is characterized by its low energy consumption, high speed and small size compared with CMOS. Inverter and majority gate are the basic building blocks for QCA circuits where it can design the most logical circuit using these gates with help of QCA wire. Due to the lack of availability of review papers, this paper will be a destination for many people who are interested in the QCA field and to know how it works and why it had taken lots of attention recentl
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