559 research outputs found
Memristors for the Curious Outsiders
We present both an overview and a perspective of recent experimental advances
and proposed new approaches to performing computation using memristors. A
memristor is a 2-terminal passive component with a dynamic resistance depending
on an internal parameter. We provide an brief historical introduction, as well
as an overview over the physical mechanism that lead to memristive behavior.
This review is meant to guide nonpractitioners in the field of memristive
circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page
Asymptotic behavior of memristive circuits
The interest in memristors has risen due to their possible application both
as memory units and as computational devices in combination with CMOS. This is
in part due to their nonlinear dynamics, and a strong dependence on the circuit
topology. We provide evidence that also purely memristive circuits can be
employed for computational purposes. In the present paper we show that a
polynomial Lyapunov function in the memory parameters exists for the case of DC
controlled memristors. Such Lyapunov function can be asymptotically
approximated with binary variables, and mapped to quadratic combinatorial
optimization problems. This also shows a direct parallel between memristive
circuits and the Hopfield-Little model. In the case of Erdos-Renyi random
circuits, we show numerically that the distribution of the matrix elements of
the projectors can be roughly approximated with a Gaussian distribution, and
that it scales with the inverse square root of the number of elements. This
provides an approximated but direct connection with the physics of disordered
system and, in particular, of mean field spin glasses. Using this and the fact
that the interaction is controlled by a projector operator on the loop space of
the circuit. We estimate the number of stationary points of the approximate
Lyapunov function and provide a scaling formula as an upper bound in terms of
the circuit topology only.Comment: 20 pages, 8 figures; proofs corrected, figures changed; results
substantially unchanged; to appear in Entrop
Towards Ultra-High Performance and Energy Efficiency of Deep Learning Systems: An Algorithm-Hardware Co-Optimization Framework
Hardware accelerations of deep learning systems have been extensively
investigated in industry and academia. The aim of this paper is to achieve
ultra-high energy efficiency and performance for hardware implementations of
deep neural networks (DNNs). An algorithm-hardware co-optimization framework is
developed, which is applicable to different DNN types, sizes, and application
scenarios. The algorithm part adopts the general block-circulant matrices to
achieve a fine-grained tradeoff between accuracy and compression ratio. It
applies to both fully-connected and convolutional layers and contains a
mathematically rigorous proof of the effectiveness of the method. The proposed
algorithm reduces computational complexity per layer from O() to O() and storage complexity from O() to O(), both for training and
inference. The hardware part consists of highly efficient Field Programmable
Gate Array (FPGA)-based implementations using effective reconfiguration, batch
processing, deep pipelining, resource re-using, and hierarchical control.
Experimental results demonstrate that the proposed framework achieves at least
152X speedup and 71X energy efficiency gain compared with IBM TrueNorth
processor under the same test accuracy. It achieves at least 31X energy
efficiency gain compared with the reference FPGA-based work.Comment: 6 figures, AAAI Conference on Artificial Intelligence, 201
First order devices, hybrid memristors, and the frontiers of nonlinear circuit theory
Several devices exhibiting memory effects have shown up in nonlinear circuit
theory in recent years. Among others, these circuit elements include Chua's
memristors, as well as memcapacitors and meminductors. These and other related
devices seem to be beyond the, say, classical scope of circuit theory, which is
formulated in terms of resistors, capacitors, inductors, and voltage and
current sources. We explore in this paper the potential extent of nonlinear
circuit theory by classifying such mem-devices in terms of the variables
involved in their constitutive relations and the notions of the differential-
and the state-order of a device. Within this framework, the frontier of first
order circuit theory is defined by so-called hybrid memristors, which are
proposed here to accommodate a characteristic relating all four fundamental
circuit variables. Devices with differential order two and mem-systems are
discussed in less detail. We allow for fully nonlinear characteristics in all
circuit elements, arriving at a rather exhaustive taxonomy of C^1-devices.
Additionally, we extend the notion of a topologically degenerate configuration
to circuits with memcapacitors, meminductors and all types of memristors, and
characterize the differential-algebraic index of nodal models of such circuits.Comment: Published in 2013. Journal reference included as a footnote in the
first pag
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