15 research outputs found
Implementing Bayesian Networks with Embedded Stochastic MRAM
Magnetic tunnel junctions (MTJ's) with low barrier magnets have been used to
implement random number generators (RNG's) and it has recently been shown that
such an MTJ connected to the drain of a conventional transistor provides a
three-terminal tunable RNG or a -bit. In this letter we show how this
-bit can be used to build a -circuit that emulates a Bayesian network
(BN), such that the correlations in real world variables can be obtained from
electrical measurements on the corresponding circuit nodes. The -circuit
design proceeds in two steps: the BN is first translated into a behavioral
model, called Probabilistic Spin Logic (PSL), defined by dimensionless biasing
(h) and interconnection (J) coefficients, which are then translated into
electronic circuit elements. As a benchmark example, we mimic a family tree of
three generations and show that the genetic relatedness calculated from a
SPICE-compatible circuit simulator matches well-known results
A bio-plausible design for visual attitude stabilization
We consider the problem of attitude stabilization using exclusively visual sensory input, and we look for a solution which can satisfy the constraints of a "bio-plausible" computation. We obtain a PD controller which is a bilinear form of the goal image, and the current and delayed visual input. Moreover, this controller can be learned using classic neural networks algorithms. The structure of the resulting computation, derived from general principles by imposing a bilinear computation, has striking resemblances with existing models for visual information processing in insects (Reichardt Correlators and lobula plate tangential cells). We validate the algorithms using faithful simulations of the fruit fly visual input
Algorithmic Foundations of Inexact Computing
Inexact computing also referred to as approximate computing is a style of
designing algorithms and computing systems wherein the accuracy of correctness
of algorithms executing on them is deliberately traded for significant resource
savings. Significant progress has been reported in this regard both in terms of
hardware as well as software or custom algorithms that exploited this approach
resulting in some loss in solution quality (accuracy) while garnering
disproportionately high savings. However, these approaches tended to be ad-hoc
and were tied to specific algorithms and technologies. Consequently, a
principled approach to designing and analyzing algorithms was lacking.
In this paper, we provide a novel model which allows us to characterize the
behavior of algorithms designed to be inexact, as well as characterize
opportunities and benefits that this approach offers. Our methods therefore are
amenable to standard asymptotic analysis and provides a clean unified
abstraction through which an algorithm's design and analysis can be conducted.
With this as a backdrop, we show that inexactness can be significantly
beneficial for some fundamental problems in that the quality of a solution can
be exponentially better if one exploits inexactness when compared to approaches
that are agnostic and are unable to exploit this approach. We show that such
gains are possible in the context of evaluating Boolean functions rooted in the
theory of Boolean functions and their spectra, PAC learning, and sorting.
Formally, this is accomplished by introducing the twin concepts of inexactness
aware and inexactness oblivious approaches to designing algorithms and the
exponential gains are shown in the context of taking the ratio of the quality
of the solution using the "aware" approach to the "oblivious" approach
A bio-plausible design for visual attitude stabilization
We consider the problem of attitude stabilization using exclusively visual sensory input, and we look for a solution which can satisfy the constraints of a "bio-plausible" computation. We obtain a PD controller which is a bilinear form of the goal image, and the current and delayed visual input. Moreover, this controller can be learned using classic neural networks algorithms. The structure of the resulting computation, derived from general principles by imposing a bilinear computation, has striking resemblances with existing models for visual information processing in insects (Reichardt Correlators and lobula plate tangential cells). We validate the algorithms using faithful simulations of the fruit fly visual input
Random Neural Networks and Optimisation
In this thesis we introduce new models and learning algorithms for the Random
Neural Network (RNN), and we develop RNN-based and other approaches for the
solution of emergency management optimisation problems.
With respect to RNN developments, two novel supervised learning algorithms are
proposed. The first, is a gradient descent algorithm for an RNN extension model
that we have introduced, the RNN with synchronised interactions (RNNSI), which
was inspired from the synchronised firing activity observed in brain neural circuits.
The second algorithm is based on modelling the signal-flow equations in RNN as a
nonnegative least squares (NNLS) problem. NNLS is solved using a limited-memory
quasi-Newton algorithm specifically designed for the RNN case.
Regarding the investigation of emergency management optimisation problems,
we examine combinatorial assignment problems that require fast, distributed and
close to optimal solution, under information uncertainty. We consider three different
problems with the above characteristics associated with the assignment of
emergency units to incidents with injured civilians (AEUI), the assignment of assets
to tasks under execution uncertainty (ATAU), and the deployment of a robotic
network to establish communication with trapped civilians (DRNCTC).
AEUI is solved by training an RNN tool with instances of the optimisation problem
and then using the trained RNN for decision making; training is achieved using
the developed learning algorithms. For the solution of ATAU problem, we introduce
two different approaches. The first is based on mapping parameters of the
optimisation problem to RNN parameters, and the second on solving a sequence of
minimum cost flow problems on appropriately constructed networks with estimated
arc costs. For the exact solution of DRNCTC problem, we develop a mixed-integer
linear programming formulation, which is based on network flows. Finally, we design
and implement distributed heuristic algorithms for the deployment of robots
when the civilian locations are known or uncertain
Best-Effort Communication Improves Performance and Scales Robustly on Conventional Hardware
Here, we test the performance and scalability of fully-asynchronous,
best-effort communication on existing, commercially-available HPC hardware.
A first set of experiments tested whether best-effort communication
strategies can benefit performance compared to the traditional perfect
communication model. At high CPU counts, best-effort communication improved
both the number of computational steps executed per unit time and the solution
quality achieved within a fixed-duration run window.
Under the best-effort model, characterizing the distribution of quality of
service across processing components and over time is critical to understanding
the actual computation being performed. Additionally, a complete picture of
scalability under the best-effort model requires analysis of how such quality
of service fares at scale. To answer these questions, we designed and measured
a suite of quality of service metrics: simulation update period, message
latency, message delivery failure rate, and message delivery coagulation. Under
a lower communication-intensivity benchmark parameterization, we found that
median values for all quality of service metrics were stable when scaling from
64 to 256 process. Under maximal communication intensivity, we found only minor
-- and, in most cases, nil -- degradation in median quality of service.
In an additional set of experiments, we tested the effect of an apparently
faulty compute node on performance and quality of service. Despite extreme
quality of service degradation among that node and its clique, median
performance and quality of service remained stable