370 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
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
Spatio-temporal Learning with Arrays of Analog Nanosynapses
Emerging nanodevices such as resistive memories are being considered for
hardware realizations of a variety of artificial neural networks (ANNs),
including highly promising online variants of the learning approaches known as
reservoir computing (RC) and the extreme learning machine (ELM). We propose an
RC/ELM inspired learning system built with nanosynapses that performs both
on-chip projection and regression operations. To address time-dynamic tasks,
the hidden neurons of our system perform spatio-temporal integration and can be
further enhanced with variable sampling or multiple activation windows. We
detail the system and show its use in conjunction with a highly analog
nanosynapse device on a standard task with intrinsic timing dynamics- the TI-46
battery of spoken digits. The system achieves nearly perfect (99%) accuracy at
sufficient hidden layer size, which compares favorably with software results.
In addition, the model is extended to a larger dataset, the MNIST database of
handwritten digits. By translating the database into the time domain and using
variable integration windows, up to 95% classification accuracy is achieved. In
addition to an intrinsically low-power programming style, the proposed
architecture learns very quickly and can easily be converted into a spiking
system with negligible loss in performance- all features that confer
significant energy efficiency.Comment: 6 pages, 3 figures. Presented at 2017 IEEE/ACM Symposium on Nanoscale
architectures (NANOARCH
A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)
Neuromorphic computing systems comprise networks of neurons that use
asynchronous events for both computation and communication. This type of
representation offers several advantages in terms of bandwidth and power
consumption in neuromorphic electronic systems. However, managing the traffic
of asynchronous events in large scale systems is a daunting task, both in terms
of circuit complexity and memory requirements. Here we present a novel routing
methodology that employs both hierarchical and mesh routing strategies and
combines heterogeneous memory structures for minimizing both memory
requirements and latency, while maximizing programming flexibility to support a
wide range of event-based neural network architectures, through parameter
configuration. We validated the proposed scheme in a prototype multi-core
neuromorphic processor chip that employs hybrid analog/digital circuits for
emulating synapse and neuron dynamics together with asynchronous digital
circuits for managing the address-event traffic. We present a theoretical
analysis of the proposed connectivity scheme, describe the methods and circuits
used to implement such scheme, and characterize the prototype chip. Finally, we
demonstrate the use of the neuromorphic processor with a convolutional neural
network for the real-time classification of visual symbols being flashed to a
dynamic vision sensor (DVS) at high speed.Comment: 17 pages, 14 figure
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