3,513 research outputs found
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
X-Rel: Energy-Efficient and Low-Overhead Approximate Reliability Framework for Error-Tolerant Applications Deployed in Critical Systems
Triple Modular Redundancy (TMR) is one of the most common techniques in
fault-tolerant systems, in which the output is determined by a majority voter.
However, the design diversity of replicated modules and/or soft errors that are
more likely to happen in the nanoscale era may affect the majority voting
scheme. Besides, the significant overheads of the TMR scheme may limit its
usage in energy consumption and area-constrained critical systems. However, for
most inherently error-resilient applications such as image processing and
vision deployed in critical systems (like autonomous vehicles and robotics),
achieving a given level of reliability has more priority than precise results.
Therefore, these applications can benefit from the approximate computing
paradigm to achieve higher energy efficiency and a lower area. This paper
proposes an energy-efficient approximate reliability (X-Rel) framework to
overcome the aforementioned challenges of the TMR systems and get the full
potential of approximate computing without sacrificing the desired reliability
constraint and output quality. The X-Rel framework relies on relaxing the
precision of the voter based on a systematical error bounding method that
leverages user-defined quality and reliability constraints. Afterward, the size
of the achieved voter is used to approximate the TMR modules such that the
overall area and energy consumption are minimized. The effectiveness of
employing the proposed X-Rel technique in a TMR structure, for different
quality constraints as well as with various reliability bounds are evaluated in
a 15-nm FinFET technology. The results of the X-Rel voter show delay, area, and
energy consumption reductions of up to 86%, 87%, and 98%, respectively, when
compared to those of the state-of-the-art approximate TMR voters.Comment: This paper has been published in IEEE Transactions on Very Large
Scale Integration (VLSI) System
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
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