166 research outputs found
Error-triggered Three-Factor Learning Dynamics for Crossbar Arrays
Recent breakthroughs suggest that local, approximate gradient descent
learning is compatible with Spiking Neural Networks (SNNs). Although SNNs can
be scalably implemented using neuromorphic VLSI, an architecture that can learn
in-situ as accurately as conventional processors is still missing. Here, we
propose a subthreshold circuit architecture designed through insights obtained
from machine learning and computational neuroscience that could achieve such
accuracy. Using a surrogate gradient learning framework, we derive local,
error-triggered learning dynamics compatible with crossbar arrays and the
temporal dynamics of SNNs. The derivation reveals that circuits used for
inference and training dynamics can be shared, which simplifies the circuit and
suppresses the effects of fabrication mismatch. We present SPICE simulations on
XFAB 180nm process, as well as large-scale simulations of the spiking neural
networks on event-based benchmarks, including a gesture recognition task. Our
results show that the number of updates can be reduced hundred-fold compared to
the standard rule while achieving performances that are on par with the
state-of-the-art
Physical Multi-Layer Phantoms for Intra-Body Communications
This paper presents approaches to creating tissue mimicking materials that
can be used as phantoms for evaluating the performance of Body Area Networks
(BAN). The main goal of the paper is to describe a methodology to create a
repeatable experimental BAN platform that can be customized depending on the
BAN scenario under test. Comparisons between different material compositions
and percentages are shown, along with the resulting electrical properties of
each mixture over the frequency range of interest for intra-body
communications; 100 KHz to 100 MHz. Test results on a composite multi-layer
sample are presented confirming the efficacy of the proposed methodology. To
date, this is the first paper that provides guidance on how to decide on
concentration levels of ingredients, depending on the exact frequency range of
operation, and the desired matched electrical characteristics (conductivity vs.
permittivity), to create multi-layer phantoms for intra-body communication
applications
AudioFool: Fast, Universal and synchronization-free Cross-Domain Attack on Speech Recognition
Automatic Speech Recognition systems have been shown to be vulnerable to
adversarial attacks that manipulate the command executed on the device. Recent
research has focused on exploring methods to create such attacks, however, some
issues relating to Over-The-Air (OTA) attacks have not been properly addressed.
In our work, we examine the needed properties of robust attacks compatible with
the OTA model, and we design a method of generating attacks with arbitrary such
desired properties, namely the invariance to synchronization, and the
robustness to filtering: this allows a Denial-of-Service (DoS) attack against
ASR systems. We achieve these characteristics by constructing attacks in a
modified frequency domain through an inverse Fourier transform. We evaluate our
method on standard keyword classification tasks and analyze it in OTA, and we
analyze the properties of the cross-domain attacks to explain the efficiency of
the approach.Comment: 10 pages, 11 Figure
A case study of mapping a software-defined radio (SDR) application on a reconfigurable DSP core
Mobile collaborative video
The emergence of pico projectors as a part of future mobile devices presents unique opportunities for collaborative settings, especially in entertainment applications, such as video playback. By aggregating pico projectors from several users, it is possible to enhance resolution, brightness, or frame rate. In this paper we present a camera-based methodology for the alignment and synchronization of multiple projectors. The approach does not require any complicated ad hoc network setup among the mobile devices. A prototype system has been set up and used to test the proposed techniques
State Dependent Statistical Timing Model for Voltage Scaled Circuits
This paper presents a novel statistical state-dependent timing model for
voltage over scaled (VoS) logic circuits that accurately and rapidly finds the
timing distribution of output bits. Using this model erroneous VoS circuits can
be represented as error-free circuits combined with an error-injector. A case
study of a two point DFT unit employing the proposed model is presented and
compared to HSPICE circuit simulation. Results show an accurate match, with
significant speedup gains
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