241 research outputs found
Neuro-memristive Circuits for Edge Computing: A review
The volume, veracity, variability, and velocity of data produced from the
ever-increasing network of sensors connected to Internet pose challenges for
power management, scalability, and sustainability of cloud computing
infrastructure. Increasing the data processing capability of edge computing
devices at lower power requirements can reduce several overheads for cloud
computing solutions. This paper provides the review of neuromorphic
CMOS-memristive architectures that can be integrated into edge computing
devices. We discuss why the neuromorphic architectures are useful for edge
devices and show the advantages, drawbacks and open problems in the field of
neuro-memristive circuits for edge computing
Experimental study of artificial neural networks using a digital memristor simulator
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a fully digital implementation of a memristor hardware simulator, as the core of an emulator, based on a behavioral model of voltage-controlled threshold-type bipolar memristors. Compared to other analog solutions, the proposed digital design is compact, easily reconfigurable, demonstrates very good matching with the mathematical model on which it is based, and complies with all the required features for memristor emulators. We validated its functionality using Altera Quartus II and ModelSim tools targeting low-cost yet powerful field programmable gate array (FPGA) families. We tested its suitability for complex memristive circuits as well as its synapse functioning in artificial neural networks (ANNs), implementing examples of associative memory and unsupervised learning of spatio-temporal correlations in parallel input streams using a simplified STDP. We provide the full circuit schematics of all our digital circuit designs and comment on the required hardware resources and their scaling trends, thus presenting a design framework for applications based on our hardware simulator.Peer ReviewedPostprint (author's final draft
Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective
On metrics of density and power efficiency, neuromorphic technologies have
the potential to surpass mainstream computing technologies in tasks where
real-time functionality, adaptability, and autonomy are essential. While
algorithmic advances in neuromorphic computing are proceeding successfully, the
potential of memristors to improve neuromorphic computing have not yet born
fruit, primarily because they are often used as a drop-in replacement to
conventional memory. However, interdisciplinary approaches anchored in machine
learning theory suggest that multifactor plasticity rules matching neural and
synaptic dynamics to the device capabilities can take better advantage of
memristor dynamics and its stochasticity. Furthermore, such plasticity rules
generally show much higher performance than that of classical Spike Time
Dependent Plasticity (STDP) rules. This chapter reviews the recent development
in learning with spiking neural network models and their possible
implementation with memristor-based hardware
Teaching Memory Circuit Elements via Experiment-Based Learning
The class of memory circuit elements which comprises memristive,
memcapacitive, and meminductive systems, is gaining considerable attention in a
broad range of disciplines. This is due to the enormous flexibility these
elements provide in solving diverse problems in analog/neuromorphic and
digital/quantum computation; the possibility to use them in an integrated
computing-memory paradigm, massively-parallel solution of different
optimization problems, learning, neural networks, etc. The time is therefore
ripe to introduce these elements to the next generation of physicists and
engineers with appropriate teaching tools that can be easily implemented in
undergraduate teaching laboratories. In this paper, we suggest the use of
easy-to-build emulators to provide a hands-on experience for the students to
learn the fundamental properties and realize several applications of these
memelements. We provide explicit examples of problems that could be tackled
with these emulators that range in difficulty from the demonstration of the
basic properties of memristive, memcapacitive, and meminductive systems to
logic/computation and cross-bar memory. The emulators can be built from
off-the-shelf components, with a total cost of a few tens of dollars, thus
providing a relatively inexpensive platform for the implementation of these
exercises in the classroom. We anticipate that this experiment-based learning
can be easily adopted and expanded by the instructors with many more case
studies.Comment: IEEE Circuits and Systems Magazine (in press
- …