27,749 research outputs found
Neuro-memristive Circuits for Edge Computing: A review
The volume, veracity, variability, and velocity of data produced from the
ever-increasing network of sensors connected to Internet pose challenges for
power management, scalability, and sustainability of cloud computing
infrastructure. Increasing the data processing capability of edge computing
devices at lower power requirements can reduce several overheads for cloud
computing solutions. This paper provides the review of neuromorphic
CMOS-memristive architectures that can be integrated into edge computing
devices. We discuss why the neuromorphic architectures are useful for edge
devices and show the advantages, drawbacks and open problems in the field of
neuro-memristive circuits for edge computing
A survey of carbon nanotube interconnects for energy efficient integrated circuits
This article is a review of the state-of-art carbon nanotube interconnects for Silicon application with respect to the recent literature. Amongst all the research on carbon nanotube interconnects, those discussed here cover 1) challenges with current copper interconnects, 2) process & growth of carbon nanotube interconnects compatible with back-end-of-line integration, and 3) modeling and simulation for circuit-level benchmarking and performance prediction. The focus is on the evolution of carbon nanotube interconnects from the process, theoretical modeling, and experimental characterization to on-chip interconnect applications. We provide an overview of the current advancements on carbon nanotube interconnects and also regarding the prospects for designing energy efficient integrated circuits. Each selected category is presented in an accessible manner aiming to serve as a survey and informative cornerstone on carbon nanotube interconnects relevant to students and scientists belonging to a range of fields from physics, processing to circuit design
Recommended from our members
Fast, non-monte-carlo estimation of transient performance variation due to device mismatch
This paper describes an efficient way of simulating the effects of device random mismatch on circuit transient characteristics, such as variations in delay or in frequency. The proposed method models DC random offsets as equivalent AC pseudo-noises and leverages the fast, linear periodically time-varying (LPTV) noise analysis available from RF circuit simulators. Therefore, the method can be considered as an extension to DC match analysis and offers a large speed-up compared to the traditional Monte-Carlo analysis. Although the assumed linear perturbation model is valid only for small variations, it enables easy ways to estimate correlations among variations and identify the most sensitive design parameters to mismatch, all at no additional simulation cost. Three benchmarks measuring the variations in the input offset voltage of a clocked comparator, the delay of a logic path, and the frequency of an oscillator demonstrate the speed improvement of about 100-1000x compared to a 1000-point Monte-Carlo method
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
Limits on Fundamental Limits to Computation
An indispensable part of our lives, computing has also become essential to
industries and governments. Steady improvements in computer hardware have been
supported by periodic doubling of transistor densities in integrated circuits
over the last fifty years. Such Moore scaling now requires increasingly heroic
efforts, stimulating research in alternative hardware and stirring controversy.
To help evaluate emerging technologies and enrich our understanding of
integrated-circuit scaling, we review fundamental limits to computation: in
manufacturing, energy, physical space, design and verification effort, and
algorithms. To outline what is achievable in principle and in practice, we
recall how some limits were circumvented, compare loose and tight limits. We
also point out that engineering difficulties encountered by emerging
technologies may indicate yet-unknown limits.Comment: 15 pages, 4 figures, 1 tabl
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
- …