32,453 research outputs found
Efficient Switches in Biology and Computer Science
Biological systems are adapted to respond quickly to changes in their environment. Signal processing often leads to all-or-none switch-like activation of downstream pathways. Such biological switches are based on molecular interactions that form positive feedback loops. Proper signal processing and switching have to be made by the noisy interactions of fluctuating molecular components; still, switching has to happen quickly once a threshold in the input signal is reached. Several computing algorithms have been designed to perform similar all-or-none decisions with high efficiency. We discuss here how the structure and dynamical features of a computational algorithm resemble the behaviour of a large class of biological switches and what makes them work efficiently. Furthermore, we highlight what biologists can learn by looking at specific features of computational algorithms
Dynamic Power Management for Neuromorphic Many-Core Systems
This work presents a dynamic power management architecture for neuromorphic
many core systems such as SpiNNaker. A fast dynamic voltage and frequency
scaling (DVFS) technique is presented which allows the processing elements (PE)
to change their supply voltage and clock frequency individually and
autonomously within less than 100 ns. This is employed by the neuromorphic
simulation software flow, which defines the performance level (PL) of the PE
based on the actual workload within each simulation cycle. A test chip in 28 nm
SLP CMOS technology has been implemented. It includes 4 PEs which can be scaled
from 0.7 V to 1.0 V with frequencies from 125 MHz to 500 MHz at three distinct
PLs. By measurement of three neuromorphic benchmarks it is shown that the total
PE power consumption can be reduced by 75%, with 80% baseline power reduction
and a 50% reduction of energy per neuron and synapse computation, all while
maintaining temporary peak system performance to achieve biological real-time
operation of the system. A numerical model of this power management model is
derived which allows DVFS architecture exploration for neuromorphics. The
proposed technique is to be used for the second generation SpiNNaker
neuromorphic many core system
Design of the Artificial: lessons from the biological roots of general intelligence
Our desire and fascination with intelligent machines dates back to the
antiquity's mythical automaton Talos, Aristotle's mode of mechanical thought
(syllogism) and Heron of Alexandria's mechanical machines and automata.
However, the quest for Artificial General Intelligence (AGI) is troubled with
repeated failures of strategies and approaches throughout the history. This
decade has seen a shift in interest towards bio-inspired software and hardware,
with the assumption that such mimicry entails intelligence. Though these steps
are fruitful in certain directions and have advanced automation, their singular
design focus renders them highly inefficient in achieving AGI. Which set of
requirements have to be met in the design of AGI? What are the limits in the
design of the artificial? Here, a careful examination of computation in
biological systems hints that evolutionary tinkering of contextual processing
of information enabled by a hierarchical architecture is the key to build AGI.Comment: Theoretical perspective on AGI (Artificial General Intelligence
A minimal model for spontaneous cell polarization and edge activity in oscillating, rotating and migrating cells
How the cells break symmetry and organize their edge activity to move
directionally is a fun- damental question in cell biology. Physical models of
cell motility commonly rely on gradients of regulatory factors and/or feedback
from the motion itself to describe polarization of edge activity. Theses
approaches, however, fail to explain cell behavior prior to the onset of
polarization. Our analysis using the model system of polarizing and moving fish
epidermal keratocytes suggests a novel and simple principle of
self-organization of cell activity in which local cell-edge dynamics depends on
the distance from the cell center, but not on the orientation with respect to
the front-back axis. We validate this principle with a stochastic model that
faithfully reproduces a range of cell-migration behaviors. Our findings
indicate that spontaneous polarization, persistent motion, and cell shape are
emergent properties of the local cell-edge dynamics controlled by the distance
from the cell center.Comment: 8 pages, 5 figure
Language-based Abstractions for Dynamical Systems
Ordinary differential equations (ODEs) are the primary means to modelling
dynamical systems in many natural and engineering sciences. The number of
equations required to describe a system with high heterogeneity limits our
capability of effectively performing analyses. This has motivated a large body
of research, across many disciplines, into abstraction techniques that provide
smaller ODE systems while preserving the original dynamics in some appropriate
sense. In this paper we give an overview of a recently proposed
computer-science perspective to this problem, where ODE reduction is recast to
finding an appropriate equivalence relation over ODE variables, akin to
classical models of computation based on labelled transition systems.Comment: In Proceedings QAPL 2017, arXiv:1707.0366
Noise-based information processing: Noise-based logic and computing: what do we have so far?
We briefly introduce noise-based logic. After describing the main motivations
we outline classical, instantaneous (squeezed and non-squeezed), continuum,
spike and random-telegraph-signal based schemes with applications such as
circuits that emulate the brain functioning and string verification via a slow
communication channel.Comment: Invited talk at the 21st International Conference on Noise and
Fluctuations, Toronto, Canada, June 12-16, 201
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