11 research outputs found
XBioSiP: A Methodology for Approximate Bio-Signal Processing at the Edge
Bio-signals exhibit high redundancy, and the algorithms for their processing
are inherently error resilient. This property can be leveraged to improve the
energy-efficiency of IoT-Edge (wearables) through the emerging trend of
approximate computing. This paper presents XBioSiP, a novel methodology for
approximate bio-signal processing that employs two quality evaluation stages,
during the pre-processing and bio-signal processing stages, to determine the
approximation parameters. It thereby achieves high energy savings while
satisfying the user-determined quality constraint. Our methodology achieves, up
to 19x and 22x reduction in the energy consumption of a QRS peak detection
algorithm for 0% and <1% loss in peak detection accuracy, respectively.Comment: Accepted for publication at the Design Automation Conference 2019
(DAC'19), Las Vegas, Nevada, US
BoundaryCAM: A Boundary-based Refinement Framework for Weakly Supervised Semantic Segmentation of Medical Images
Weakly Supervised Semantic Segmentation (WSSS) with only image-level
supervision is a promising approach to deal with the need for Segmentation
networks, especially for generating a large number of pixel-wise masks in a
given dataset. However, most state-of-the-art image-level WSSS techniques lack
an understanding of the geometric features embedded in the images since the
network cannot derive any object boundary information from just image-level
labels. We define a boundary here as the line separating an object and its
background, or two different objects. To address this drawback, we propose our
novel BoundaryCAM framework, which deploys state-of-the-art class activation
maps combined with various post-processing techniques in order to achieve
fine-grained higher-accuracy segmentation masks. To achieve this, we
investigate a state-of-the-art unsupervised semantic segmentation network that
can be used to construct a boundary map, which enables BoundaryCAM to predict
object locations with sharper boundaries. By applying our method to WSSS
predictions, we were able to achieve up to 10% improvements even to the benefit
of the current state-of-the-art WSSS methods for medical imaging. The framework
is open-source and accessible online at
https://github.com/bharathprabakaran/BoundaryCAM
Image Label based Semantic Segmentation Framework using Object Perimeters
Achieving high-quality semantic segmentation predictions using only
image-level labels enables a new level of real-world applicability. Although
state-of-the-art networks deliver reliable predictions, the amount of
handcrafted pixel-wise annotations to enable these results are not feasible in
many real-world applications. Hence, several works have already targeted this
bottleneck, using classifier-based networks like Class Activation Maps (CAMs)
as a base. Addressing CAM's weaknesses of fuzzy borders and incomplete
predictions, state-of-the-art approaches rely only on adding regulations to the
classifier loss or using pixel-similarity-based refinement after the fact. We
propose a framework that introduces an additional module using object
perimeters for improved saliency. We define object perimeter information as the
line separating the object and background. Our new PerimeterFit module will be
applied to pre-refine the CAM predictions before using the
pixel-similarity-based network. In this way, our PerimeterFit increases the
quality of the CAM prediction while simultaneously improving the false negative
rate. We investigated a wide range of state-of-the-art unsupervised semantic
segmentation networks and edge detection techniques to create useful perimeter
maps, which enable our framework to predict object locations with sharper
perimeters. We achieved up to 1.5\% improvement over frameworks without our
PerimeterFit module. We conduct an exhaustive analysis to illustrate that our
framework enhances existing state-of-the-art frameworks for image-level-based
semantic segmentation. The framework is open-source and accessible online at
https://github.com/ErikOstrowski/Perimeter-based-Semantic-Segmentation
FPUS23: An Ultrasound Fetus Phantom Dataset with Deep Neural Network Evaluations for Fetus Orientations, Fetal Planes, and Anatomical Features
Ultrasound imaging is one of the most prominent technologies to evaluate the
growth, progression, and overall health of a fetus during its gestation.
However, the interpretation of the data obtained from such studies is best left
to expert physicians and technicians who are trained and well-versed in
analyzing such images. To improve the clinical workflow and potentially develop
an at-home ultrasound-based fetal monitoring platform, we present a novel fetus
phantom ultrasound dataset, FPUS23, which can be used to identify (1) the
correct diagnostic planes for estimating fetal biometric values, (2) fetus
orientation, (3) their anatomical features, and (4) bounding boxes of the fetus
phantom anatomies at 23 weeks gestation. The entire dataset is composed of
15,728 images, which are used to train four different Deep Neural Network
models, built upon a ResNet34 backbone, for detecting aforementioned fetus
features and use-cases. We have also evaluated the models trained using our
FPUS23 dataset, to show that the information learned by these models can be
used to substantially increase the accuracy on real-world ultrasound fetus
datasets. We make the FPUS23 dataset and the pre-trained models publicly
accessible at https://github.com/bharathprabakaran/FPUS23, which will further
facilitate future research on fetal ultrasound imaging and analysis
UnbiasedNets: A Dataset Diversification Framework for Robustness Bias Alleviation in Neural Networks
Performance of trained neural network (NN) models, in terms of testing
accuracy, has improved remarkably over the past several years, especially with
the advent of deep learning. However, even the most accurate NNs can be biased
toward a specific output classification due to the inherent bias in the
available training datasets, which may propagate to the real-world
implementations. This paper deals with the robustness bias, i.e., the bias
exhibited by the trained NN by having a significantly large robustness to noise
for a certain output class, as compared to the remaining output classes. The
bias is shown to result from imbalanced datasets, i.e., the datasets where all
output classes are not equally represented. Towards this, we propose the
UnbiasedNets framework, which leverages K-means clustering and the NN's noise
tolerance to diversify the given training dataset, even from relatively smaller
datasets. This generates balanced datasets and reduces the bias within the
datasets themselves. To the best of our knowledge, this is the first framework
catering to the robustness bias problem in NNs. We use real-world datasets to
demonstrate the efficacy of the UnbiasedNets for data diversification, in case
of both binary and multi-label classifiers. The results are compared to
well-known tools aimed at generating balanced datasets, and illustrate how
existing works have limited success while addressing the robustness bias. In
contrast, UnbiasedNets provides a notable improvement over existing works,
while even reducing the robustness bias significantly in some cases, as
observed by comparing the NNs trained on the diversified and original datasets.Comment: Springer Machine Learning 202
EMAP: A Cloud-Edge Hybrid Framework for EEG Monitoring and Cross-Correlation Based Real-time Anomaly Prediction
State-of-the-art techniques for detecting, or predicting, neurological
disorders (1) focus on predicting each disorder individually, and are (2)
computationally expensive, leading to a delay that can potentially render the
prediction useless, especially in critical events. Towards this, we present a
real-time two-tiered framework called EMAP, which cross-correlates the input
with all the EEG signals in our mega-database (a combination of multiple EEG
datasets) at the cloud, while tracking the signal in real-time at the edge, to
predict the occurrence of a neurological anomaly. Using the proposed framework,
we have demonstrated a prediction accuracy of up to 94% for the three different
anomalies that we have tested.Comment: Accepted for Publication at the 57th Design Automation Conference
(DAC), July 2020, San Francisco, CA, US
Xel-FPGAs: An End-to-End Automated Exploration Framework for Approximate Accelerators in FPGA-Based Systems
Generation and exploration of approximate circuits and accelerators has been
a prominent research domain to achieve energy-efficiency and/or performance
improvements. This research has predominantly focused on ASICs, while not
achieving similar gains when deployed for FPGA-based accelerator systems, due
to the inherent architectural differences between the two. In this work, we
propose a novel framework, Xel-FPGAs, which leverages statistical or machine
learning models to effectively explore the architecture-space of
state-of-the-art ASIC-based approximate circuits to cater them for FPGA-based
systems given a simple RTL description of the target application. We have also
evaluated the scalability of our framework on a multi-stage application using a
hierarchical search strategy. The Xel-FPGAs framework is capable of reducing
the exploration time by up to 95%, when compared to the default synthesis,
place, and route approaches, while identifying an improved set of
Pareto-optimal designs for a given application, when compared to the
state-of-the-art. The complete framework is open-source and available online at
https://github.com/ehw-fit/xel-fpgas.Comment: Accepted for publication at the 42nd International Conference on
Computer-Aided Design (ICCAD), November 2023, San Francisco, CA, US
Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19
IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19.
Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19.
DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 nonâcritically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022).
INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (nâ=â257), ARB (nâ=â248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; nâ=â10), or no RAS inhibitor (control; nâ=â264) for up to 10 days.
MAIN OUTCOMES AND MEASURES The primary outcome was organ supportâfree days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes.
RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ supportâfree days among critically ill patients was 10 (â1 to 16) in the ACE inhibitor group (nâ=â231), 8 (â1 to 17) in the ARB group (nâ=â217), and 12 (0 to 17) in the control group (nâ=â231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ supportâfree days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively).
CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes.
TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570
Architectural-Space Exploration of Heterogeneous Reliability and Checkpointing Modes for Out-of-Order Superscalar Processors
State-of-the-art reliability techniques and mechanisms deploy full-scale redundancy, like double or triple modular redundancy (DMR, TMR), on different layers of the computing stack to detect and/or correct such transient faults. However, the techniques relying on full-scale redundancy incur significant area, performance, and/or power overheads, which might not always be feasible/practical due to system constraints such as deadlines and available power budget for the full chip (or a processor core). In this work, we propose a novel design methodology to generate and explore the architectural-space of heterogeneous reliability modes for out-of-order superscalar multi-core processors. These heterogeneous modes enable varying reliability and power/area trade-offs, from which an optimal configuration can be chosen at run time to meet the reliability requirements of a given system while reducing the corresponding power overheads (or solving the inverse problem, i.e., maximizing the reliability under a given power constraint). Our experimental results show that a pareto-optimal heterogeneous reliability mode reduces the core vulnerability by 87%, on average, across multiple application workloads, with area and power overheads of 10% and 43%, respectively. To further enhance the design space of heterogeneous reliability modes, we investigate the effectiveness of combining different processor state compression techniques like Distributed Multi-threaded Checkpointing (DMTCP), Hash-based Incremental Checkpointing (HBICT) and GNU zip, such that the correct processor state can be recovered once a fault is detected. We reduced the checkpoint sizes by a factor of ~6Ă using a unique combination of different state compression techniques