2 research outputs found
In-Place Zero-Space Memory Protection for CNN
Convolutional Neural Networks (CNN) are being actively explored for
safety-critical applications such as autonomous vehicles and aerospace, where
it is essential to ensure the reliability of inference results in the presence
of possible memory faults. Traditional methods such as error correction codes
(ECC) and Triple Modular Redundancy (TMR) are CNN-oblivious and incur
substantial memory overhead and energy cost. This paper introduces in-place
zero-space ECC assisted with a new training scheme weight distribution-oriented
training. The new method provides the first known zero space cost memory
protection for CNNs without compromising the reliability offered by traditional
ECC.Comment: Accepted in NeurIPS'1
The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications
While deep machine learning technologies are now pervasive in
state-of-the-art image recognition and natural language processing
applications, only in recent years have these technologies started to
sufficiently mature in applications related to wireless communications. In
particular, recent research has shown deep machine learning to be an enabling
technology for cognitive radio applications as well as a useful tool for
supplementing expertly defined algorithms for spectrum sensing applications
such as signal detection, estimation, and classification (termed here as Radio
Frequency Machine Learning, or RFML). A major driver for the usage of deep
machine learning in the context of wireless communications is that little, to
no, a priori knowledge of the intended spectral environment is required, given
that there is an abundance of representative data to facilitate training and
evaluation. However, in addition to this fundamental need for sufficient data,
there are other key considerations, such as trust, security, and
hardware/software issues, that must be taken into account before deploying deep
machine learning systems in real-world wireless communication applications.
This paper provides an overview and survey of prior work related to these major
research considerations. In particular, we present their unique considerations
in the RFML application space, which are not generally present in the image,
audio, and/or text application spaces