831 research outputs found
Highly Scalable Neuromorphic Hardware with 1-bit Stochastic nano-Synapses
Thermodynamic-driven filament formation in redox-based resistive memory and
the impact of thermal fluctuations on switching probability of emerging
magnetic switches are probabilistic phenomena in nature, and thus, processes of
binary switching in these nonvolatile memories are stochastic and vary from
switching cycle-to-switching cycle, in the same device, and from
device-to-device, hence, they provide a rich in-situ spatiotemporal stochastic
characteristic. This work presents a highly scalable neuromorphic hardware
based on crossbar array of 1-bit resistive crosspoints as distributed
stochastic synapses. The network shows a robust performance in emulating
selectivity of synaptic potentials in neurons of primary visual cortex to the
orientation of a visual image. The proposed model could be configured to accept
a wide range of nanodevices.Comment: 9 pages, 6 figure
A Compact CMOS Memristor Emulator Circuit and its Applications
Conceptual memristors have recently gathered wider interest due to their
diverse application in non-von Neumann computing, machine learning,
neuromorphic computing, and chaotic circuits. We introduce a compact CMOS
circuit that emulates idealized memristor characteristics and can bridge the
gap between concepts to chip-scale realization by transcending device
challenges. The CMOS memristor circuit embodies a two-terminal variable
resistor whose resistance is controlled by the voltage applied across its
terminals. The memristor 'state' is held in a capacitor that controls the
resistor value. This work presents the design and simulation of the memristor
emulation circuit, and applies it to a memcomputing application of maze solving
using analog parallelism. Furthermore, the memristor emulator circuit can be
designed and fabricated using standard commercial CMOS technologies and opens
doors to interesting applications in neuromorphic and machine learning
circuits.Comment: Submitted to International Symposium of Circuits and Systems (ISCAS)
201
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
Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition
A neuromorphic chip that combines CMOS analog spiking neurons and memristive
synapses offers a promising solution to brain-inspired computing, as it can
provide massive neural network parallelism and density. Previous hybrid analog
CMOS-memristor approaches required extensive CMOS circuitry for training, and
thus eliminated most of the density advantages gained by the adoption of
memristor synapses. Further, they used different waveforms for pre and
post-synaptic spikes that added undesirable circuit overhead. Here we describe
a hardware architecture that can feature a large number of memristor synapses
to learn real-world patterns. We present a versatile CMOS neuron that combines
integrate-and-fire behavior, drives passive memristors and implements
competitive learning in a compact circuit module, and enables in-situ
plasticity in the memristor synapses. We demonstrate handwritten-digits
recognition using the proposed architecture using transistor-level circuit
simulations. As the described neuromorphic architecture is homogeneous, it
realizes a fundamental building block for large-scale energy-efficient
brain-inspired silicon chips that could lead to next-generation cognitive
computing.Comment: This is a preprint of an article accepted for publication in IEEE
Journal on Emerging and Selected Topics in Circuits and Systems, vol 5, no.
2, June 201
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
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