260 research outputs found
An event-based architecture for solving constraint satisfaction problems
Constraint satisfaction problems (CSPs) are typically solved using
conventional von Neumann computing architectures. However, these architectures
do not reflect the distributed nature of many of these problems and are thus
ill-suited to solving them. In this paper we present a hybrid analog/digital
hardware architecture specifically designed to solve such problems. We cast
CSPs as networks of stereotyped multi-stable oscillatory elements that
communicate using digital pulses, or events. The oscillatory elements are
implemented using analog non-stochastic circuits. The non-repeating phase
relations among the oscillatory elements drive the exploration of the solution
space. We show that this hardware architecture can yield state-of-the-art
performance on a number of CSPs under reasonable assumptions on the
implementation. We present measurements from a prototype electronic chip to
demonstrate that a physical implementation of the proposed architecture is
robust to practical non-idealities and to validate the theory proposed.Comment: First two authors contributed equally to this wor
A Survey on Continuous Time Computations
We provide an overview of theories of continuous time computation. These
theories allow us to understand both the hardness of questions related to
continuous time dynamical systems and the computational power of continuous
time analog models. We survey the existing models, summarizing results, and
point to relevant references in the literature
Stochastic Memory Devices for Security and Computing
With the widespread use of mobile computing and internet of things, secured communication and chip authentication have become extremely important. Hardware-based security concepts generally provide the best performance in terms of a good standard of security, low power consumption, and large-area density. In these concepts, the stochastic properties of nanoscale devices, such as the physical and geometrical variations of the process, are harnessed for true random number generators (TRNGs) and physical unclonable functions (PUFs). Emerging memory devices, such as resistive-switching memory (RRAM), phase-change memory (PCM), and spin-transfer torque magnetic memory (STT-MRAM), rely on a unique combination of physical mechanisms for transport and switching, thus appear to be an ideal source of entropy for TRNGs and PUFs. An overview of stochastic phenomena in memory devices and their use for developing security and computing primitives is provided. First, a broad classification of methods to generate true random numbers via the stochastic properties of nanoscale devices is presented. Then, practical implementations of stochastic TRNGs, such as hardware security and stochastic computing, are shown. Finally, future challenges to stochastic memory development are discussed
In-memory computing with emerging memory devices: Status and outlook
Supporting data for "In-memory computing with emerging memory devices: status and outlook", submitted to APL Machine Learning
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
HpGAN: Sequence Search with Generative Adversarial Networks
Sequences play an important role in many engineering applications and
systems. Searching sequences with desired properties has long been an
interesting but also challenging research topic. This article proposes a novel
method, called HpGAN, to search desired sequences algorithmically using
generative adversarial networks (GAN). HpGAN is based on the idea of zero-sum
game to train a generative model, which can generate sequences with
characteristics similar to the training sequences. In HpGAN, we design the
Hopfield network as an encoder to avoid the limitations of GAN in generating
discrete data. Compared with traditional sequence construction by algebraic
tools, HpGAN is particularly suitable for intractable problems with complex
objectives which prevent mathematical analysis. We demonstrate the search
capabilities of HpGAN in two applications: 1) HpGAN successfully found many
different mutually orthogonal complementary code sets (MOCCS) and optimal
odd-length Z-complementary pairs (OB-ZCPs) which are not part of the training
set. In the literature, both MOCSSs and OB-ZCPs have found wide applications in
wireless communications. 2) HpGAN found new sequences which achieve four-times
increase of signal-to-interference ratio--benchmarked against the well-known
Legendre sequence--of a mismatched filter (MMF) estimator in pulse compression
radar systems. These sequences outperform those found by AlphaSeq.Comment: 12 pages, 16 figure
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