4,290 research outputs found
Nature-Inspired Interconnects for Self-Assembled Large-Scale Network-on-Chip Designs
Future nano-scale electronics built up from an Avogadro number of components
needs efficient, highly scalable, and robust means of communication in order to
be competitive with traditional silicon approaches. In recent years, the
Networks-on-Chip (NoC) paradigm emerged as a promising solution to interconnect
challenges in silicon-based electronics. Current NoC architectures are either
highly regular or fully customized, both of which represent implausible
assumptions for emerging bottom-up self-assembled molecular electronics that
are generally assumed to have a high degree of irregularity and imperfection.
Here, we pragmatically and experimentally investigate important design
trade-offs and properties of an irregular, abstract, yet physically plausible
3D small-world interconnect fabric that is inspired by modern network-on-chip
paradigms. We vary the framework's key parameters, such as the connectivity,
the number of switch nodes, the distribution of long- versus short-range
connections, and measure the network's relevant communication characteristics.
We further explore the robustness against link failures and the ability and
efficiency to solve a simple toy problem, the synchronization task. The results
confirm that (1) computation in irregular assemblies is a promising and
disruptive computing paradigm for self-assembled nano-scale electronics and (2)
that 3D small-world interconnect fabrics with a power-law decaying distribution
of shortcut lengths are physically plausible and have major advantages over
local 2D and 3D regular topologies
A geographically distributed bio-hybrid neural network with memristive plasticity
Throughout evolution the brain has mastered the art of processing real-world
inputs through networks of interlinked spiking neurons. Synapses have emerged
as key elements that, owing to their plasticity, are merging neuron-to-neuron
signalling with memory storage and computation. Electronics has made important
steps in emulating neurons through neuromorphic circuits and synapses with
nanoscale memristors, yet novel applications that interlink them in
heterogeneous bio-inspired and bio-hybrid architectures are just beginning to
materialise. The use of memristive technologies in brain-inspired architectures
for computing or for sensing spiking activity of biological neurons8 are only
recent examples, however interlinking brain and electronic neurons through
plasticity-driven synaptic elements has remained so far in the realm of the
imagination. Here, we demonstrate a bio-hybrid neural network (bNN) where
memristors work as "synaptors" between rat neural circuits and VLSI neurons.
The two fundamental synaptors, from artificial-to-biological (ABsyn) and from
biological-to- artificial (BAsyn), are interconnected over the Internet. The
bNN extends across Europe, collapsing spatial boundaries existing in natural
brain networks and laying the foundations of a new geographically distributed
and evolving architecture: the Internet of Neuro-electronics (IoN).Comment: 16 pages, 10 figure
Energy challenges for ICT
The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute signi - cantly to the reduction of CO2 emission and enhance resource e ciency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manu- facturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource e - ciency, a multidisciplinary ICT-energy community needs to be brought together cover- ing devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded sys- tems, e cient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging eld and a common framework to strive towards energy-sustainable ICT
Metallic Coaxial Nanolasers
The last two decades have witnessed tremendous advancements in the area of
nanophotonics and plasmonics. Undoubtedly, the introduction of metallic
structures has opened a path towards light confinement and manipulation at the
subwavelength scale { a regime that was previously thought to be out of reach
in optics. Of central importance is to devise efficient light sources to power
up the future nanoscale optical circuits. Coaxial resonators can provide a
platform to implement such subwavelength sources. They support ultrasmall
cavity modes and offer large mode-emitter overlap as well as multifold
scalability. Given their large modulation bandwidth, they hold promise for high
speed optical interconnects { where they can be used for light generation and
modulation simultaneously. In addition, the possibility of thresholdless
operation in such devices may have implications in developing the next
generation of efficient lighting systems. In this review article, the physics
and applications of coaxial nanolasers will be discussed
Multi-core devices for safety-critical systems: a survey
Multi-core devices are envisioned to support the development of next-generation safety-critical systems, enabling the on-chip integration of functions of different criticality. This integration provides multiple system-level potential benefits such as cost, size, power, and weight reduction. However, safety certification becomes a challenge and several fundamental safety technical requirements must be addressed, such as temporal and spatial independence, reliability, and diagnostic coverage. This survey provides a categorization and overview at different device abstraction levels (nanoscale, component, and device) of selected key research contributions that support the compliance with these fundamental safety requirements.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under grant TIN2015-65316-P, Basque Government under grant KK-2019-00035 and the HiPEAC Network of Excellence. The Spanish Ministry of Economy and Competitiveness has also partially supported Jaume Abella under Ramon y Cajal postdoctoral fellowship (RYC-2013-14717).Peer ReviewedPostprint (author's final draft
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
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