6,424 research outputs found
Design and Investigation of Genetic Algorithmic and Reinforcement Learning Approaches to Wire Crossing Reductions for pNML Devices
Perpendicular nanomagnet logic (pNML) is an emerging post-CMOS technology which encodes binary data in the polarization of single-domain nanomagnets and performs operations via fringing field interactions. Currently, there is no complete top-down workflow for pNML. Researchers must instead simultaneously handle place-and-route, timing, and logic minimization by hand. These tasks include multiple NP-Hard subproblems, and the lack of automated tools for solving them for pNML precludes the design of large-scale pNML circuits
Deep-PowerX: A Deep Learning-Based Framework for Low-Power Approximate Logic Synthesis
This paper aims at integrating three powerful techniques namely Deep
Learning, Approximate Computing, and Low Power Design into a strategy to
optimize logic at the synthesis level. We utilize advances in deep learning to
guide an approximate logic synthesis engine to minimize the dynamic power
consumption of a given digital CMOS circuit, subject to a predetermined error
rate at the primary outputs. Our framework, Deep-PowerX, focuses on replacing
or removing gates on a technology-mapped network and uses a Deep Neural Network
(DNN) to predict error rates at primary outputs of the circuit when a specific
part of the netlist is approximated. The primary goal of Deep-PowerX is to
reduce the dynamic power whereas area reduction serves as a secondary
objective. Using the said DNN, Deep-PowerX is able to reduce the exponential
time complexity of standard approximate logic synthesis to linear time.
Experiments are done on numerous open source benchmark circuits. Results show
significant reduction in power and area by up to 1.47 times and 1.43 times
compared to exact solutions and by up to 22% and 27% compared to
state-of-the-art approximate logic synthesis tools while having orders of
magnitudes lower run-time
Neural network agent playing spin Hamiltonian games on a quantum computer
Quantum computing is expected to provide new promising approaches for solving
the most challenging problems in material science, communication, search,
machine learning and other domains. However, due to the decoherence and gate
imperfection errors modern quantum computer systems are characterized by a very
complex, dynamical, uncertain and fluctuating computational environment. We
develop an autonomous agent effectively interacting with such an environment to
solve magnetism problems. By using the reinforcement learning the agent is
trained to find the best-possible approximation of a spin Hamiltonian ground
state from self-play on quantum devices. We show that the agent can learn the
entanglement to imitate the ground state of the quantum spin dimer. The
experiments were conducted on quantum computers provided by IBM. To compensate
the decoherence we use local spin correction procedure derived from a general
sum rule for spin-spin correlation functions of a quantum system with even
number of antiferromagnetically-coupled spins in the ground state. Our study
paves a way to create a new family of the neural network eigensolvers for
quantum computers.Comment: Local spin correction procedure was used to compensate real device
errors; comparison with variational approach was adde
Perspective: Organic electronic materials and devices for neuromorphic engineering
Neuromorphic computing and engineering has been the focus of intense research
efforts that have been intensified recently by the mutation of Information and
Communication Technologies (ICT). In fact, new computing solutions and new
hardware platforms are expected to emerge to answer to the new needs and
challenges of our societies. In this revolution, lots of candidates
technologies are explored and will require leveraging of the pro and cons. In
this perspective paper belonging to the special issue on neuromorphic
engineering of Journal of Applied Physics, we focus on the current achievements
in the field of organic electronics and the potentialities and specificities of
this research field. We highlight how unique material features available
through organic materials can be used to engineer useful and promising
bioinspired devices and circuits. We also discuss about the opportunities that
organic electronic are offering for future research directions in the
neuromorphic engineering field
Birth of a Learning Law
Defense Advanced Research Projects Agency; Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657, N00014-92-J-1309
New Ideas for Brain Modelling
This paper describes some biologically-inspired processes that could be used
to build the sort of networks that we associate with the human brain. New to
this paper, a 'refined' neuron will be proposed. This is a group of neurons
that by joining together can produce a more analogue system, but with the same
level of control and reliability that a binary neuron would have. With this new
structure, it will be possible to think of an essentially binary system in
terms of a more variable set of values. The paper also shows how recent
research associated with the new model, can be combined with established
theories, to produce a more complete picture. The propositions are largely in
line with conventional thinking, but possibly with one or two more radical
suggestions. An earlier cognitive model can be filled in with more specific
details, based on the new research results, where the components appear to fit
together almost seamlessly. The intention of the research has been to describe
plausible 'mechanical' processes that can produce the appropriate brain
structures and mechanisms, but that could be used without the magical
'intelligence' part that is still not fully understood. There are also some
important updates from an earlier version of this paper
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