101,076 research outputs found
Low Power Processor Architectures and Contemporary Techniques for Power Optimization – A Review
The technological evolution has increased the number of transistors for a given die area significantly and increased the switching speed from few MHz to GHz range. Such inversely proportional decline in size and boost in performance consequently demands shrinking of supply voltage and effective power dissipation in chips with millions of transistors. This has triggered substantial amount of research in power reduction techniques into almost every aspect of the chip and particularly the processor cores contained in the chip. This paper presents an overview of techniques for achieving the power efficiency mainly at the processor core level but also visits related domains such as buses and memories. There are various processor parameters and features such as supply voltage, clock frequency, cache and pipelining which can be optimized to reduce the power consumption of the processor. This paper discusses various ways in which these parameters can be optimized. Also, emerging power efficient processor architectures are overviewed and research activities are discussed which should help reader identify how these factors in a processor contribute to power consumption. Some of these concepts have been already established whereas others are still active research areas. © 2009 ACADEMY PUBLISHER
The Road to Quantum Computational Supremacy
We present an idiosyncratic view of the race for quantum computational
supremacy. Google's approach and IBM challenge are examined. An unexpected
side-effect of the race is the significant progress in designing fast classical
algorithms. Quantum supremacy, if achieved, won't make classical computing
obsolete.Comment: 15 pages, 1 figur
The Analogue Computer as a Voltage-Controlled Synthesiser
This paper re-appraises the role of analogue computers within electronic and
computer music and provides some pointers to future areas of research. It
begins by introducing the idea of analogue computing and placing in the context
of sound and music applications. This is followed by a brief examination of the
classic constituents of an analogue computer, contrasting these with the
typical modular voltage-controlled synthesiser. Two examples are presented,
leading to a discussion on some parallels between these two technologies. This
is followed by an examination of the current state-of-the-art in analogue
computation and its prospects for applications in computer and electronic
music
A 0.18µm CMOS DDCCII for Portable LV-LP Filters
In this paper a current mode very low voltage (LV) (1V) and low power (LP) (21 µW) differential difference second generation current conveyor (CCII) is presented. The circuit is developed by applying the current sensing technique to a fully balanced version of a differential difference amplifier (DDA) so to design a suitable LV LP integrated version of the so-called differential difference CCII (DDCCII). Post-layout results, using a 0.18µm SMIC CMOS technology, have shown good general circuit performances making the proposed circuit suitable for fully integration in battery portable systems as, for examples, fully differential Sallen-Key bandpass filter
Computers from plants we never made. Speculations
We discuss possible designs and prototypes of computing systems that could be
based on morphological development of roots, interaction of roots, and analog
electrical computation with plants, and plant-derived electronic components. In
morphological plant processors data are represented by initial configuration of
roots and configurations of sources of attractants and repellents; results of
computation are represented by topology of the roots' network. Computation is
implemented by the roots following gradients of attractants and repellents, as
well as interacting with each other. Problems solvable by plant roots, in
principle, include shortest-path, minimum spanning tree, Voronoi diagram,
-shapes, convex subdivision of concave polygons. Electrical properties
of plants can be modified by loading the plants with functional nanoparticles
or coating parts of plants of conductive polymers. Thus, we are in position to
make living variable resistors, capacitors, operational amplifiers,
multipliers, potentiometers and fixed-function generators. The electrically
modified plants can implement summation, integration with respect to time,
inversion, multiplication, exponentiation, logarithm, division. Mathematical
and engineering problems to be solved can be represented in plant root networks
of resistive or reaction elements. Developments in plant-based computing
architectures will trigger emergence of a unique community of biologists,
electronic engineering and computer scientists working together to produce
living electronic devices which future green computers will be made of.Comment: The chapter will be published in "Inspired by Nature. Computing
inspired by physics, chemistry and biology. Essays presented to Julian Miller
on the occasion of his 60th birthday", Editors: Susan Stepney and Andrew
Adamatzky (Springer, 2017
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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