2,700 research outputs found
PieceTimer: A Holistic Timing Analysis Framework Considering Setup/Hold Time Interdependency Using A Piecewise Model
In static timing analysis, clock-to-q delays of flip-flops are considered as
constants. Setup times and hold times are characterized separately and also
used as constants. The characterized delays, setup times and hold times, are
ap- plied in timing analysis independently to verify the perfor- mance of
circuits. In reality, however, clock-to-q delays of flip-flops depend on both
setup and hold times. Instead of being constants, these delays change with
respect to different setup/hold time combinations. Consequently, the simple ab-
straction of setup/hold times and constant clock-to-q delays introduces
inaccuracy in timing analysis. In this paper, we propose a holistic method to
consider the relation between clock-to-q delays and setup/hold time
combinations with a piecewise linear model. The result is more accurate than
that of traditional timing analysis, and the incorporation of the
interdependency between clock-to-q delays, setup times and hold times may also
improve circuit performance.Comment: IEEE/ACM International Conference on Computer-Aided Design (ICCAD),
November 201
Sampling-based Buffer Insertion for Post-Silicon Yield Improvement under Process Variability
At submicron manufacturing technology nodes process variations affect circuit
performance significantly. This trend leads to a large timing margin and thus
overdesign to maintain yield. To combat this pessimism, post-silicon clock
tuning buffers can be inserted into circuits to balance timing budgets of
critical paths with their neighbors. After manufacturing, these clock buffers
can be configured for each chip individually so that chips with timing failures
may be rescued to improve yield. In this paper, we propose a sampling-based
method to determine the proper locations of these buffers. The goal of this
buffer insertion is to reduce the number of buffers and their ranges, while
still maintaining a good yield improvement. Experimental results demonstrate
that our algorithm can achieve a significant yield improvement (up to 35%) with
only a small number of buffers.Comment: Design, Automation and Test in Europe (DATE), 201
Early Classification for Dynamic Inference of Neural Networks
Deep neural networks (DNNs) have been successfully applied in various fields.
In DNNs, a large number of multiply-accumulate (MAC) operations is required to
be performed, posing critical challenges in applying them in
resource-constrained platforms, e.g., edge devices. Dynamic neural networks
have been introduced to allow a structural adaption, e.g., early-exit,
according to different inputs to reduce the computational cost of DNNs.
Existing early-exit techniques deploy classifiers at intermediate layers of
DNNs to push them to make a classification decision as early as possible.
However, the learned features at early layers might not be sufficient to
exclude all the irrelevant classes and decide the correct class, leading to
suboptimal results. To address this challenge, in this paper, we propose a
class-based early-exit for dynamic inference. Instead of pushing DNNs to make a
dynamic decision at intermediate layers, we take advantages of the learned
features in these layers to exclude as many irrelevant classes as possible, so
that later layers only have to determine the target class among the remaining
classes. Until at a layer only one class remains, this class is the
corresponding classification result. To realize this class-based exclusion, we
assign each class with a classifier at intermediate layers and train the
networks together with these classifiers. Afterwards, an exclusion strategy is
developed to exclude irrelevant classes at early layers. Experimental results
demonstrate the computational cost of DNNs in inference can be reduced
significantly
Associations of Muscle Mass and Strength with All-Cause Mortality among US Older Adults
INTRODUCTION:
Recent studies suggested that muscle mass and muscle strength may independently or synergistically affect aging-related health outcomes in older adults; however, prospective data on mortality in the general population are sparse.
METHODS:
We aimed to prospectively examine individual and joint associations of low muscle mass and low muscle strength with all-cause mortality in a nationally representative sample. This study included 4449 participants age 50 yr and older from the National Health and Nutrition Examination Survey 1999 to 2002 with public use 2011 linked mortality files. Weighted multivariable logistic regression models were adjusted for age, sex, race, body mass index (BMI), smoking, alcohol use, education, leisure time physical activity, sedentary time, and comorbid diseases.
RESULTS:
Overall, the prevalence of low muscle mass was 23.1% defined by appendicular lean mass (ALM) and 17.0% defined by ALM/BMI, and the prevalence of low muscle strength was 19.4%. In the joint analyses, all-cause mortality was significantly higher among individuals with low muscle strength, whether they had low muscle mass (odds ratio [OR], 2.03; 95% confidence interval [CI], 1.27-3.24 for ALM; OR, 2.53; 95% CI, 1.64-3.88 for ALM/BMI) or not (OR, 2.66; 95% CI, 1.53-4.62 for ALM; OR, 2.17; 95% CI, 1.29-3.64 for ALM/BMI). In addition, the significant associations between low muscle strength and all-cause mortality persisted across different levels of metabolic syndrome, sedentary time, and LTPA.
CONCLUSIONS:
Low muscle strength was independently associated with elevated risk of all-cause mortality, regardless of muscle mass, metabolic syndrome, sedentary time, or LTPA among US older adults, indicating the importance of muscle strength in predicting aging-related health outcomes in older adults
Logic Design of Neural Networks for High-Throughput and Low-Power Applications
Neural networks (NNs) have been successfully deployed in various fields. In
NNs, a large number of multiplyaccumulate (MAC) operations need to be
performed. Most existing digital hardware platforms rely on parallel MAC units
to accelerate these MAC operations. However, under a given area constraint, the
number of MAC units in such platforms is limited, so MAC units have to be
reused to perform MAC operations in a neural network. Accordingly, the
throughput in generating classification results is not high, which prevents the
application of traditional hardware platforms in extreme-throughput scenarios.
Besides, the power consumption of such platforms is also high, mainly due to
data movement. To overcome this challenge, in this paper, we propose to flatten
and implement all the operations at neurons, e.g., MAC and ReLU, in a neural
network with their corresponding logic circuits. To improve the throughput and
reduce the power consumption of such logic designs, the weight values are
embedded into the MAC units to simplify the logic, which can reduce the delay
of the MAC units and the power consumption incurred by weight movement. The
retiming technique is further used to improve the throughput of the logic
circuits for neural networks. In addition, we propose a hardware-aware training
method to reduce the area of logic designs of neural networks. Experimental
results demonstrate that the proposed logic designs can achieve high throughput
and low power consumption for several high-throughput applications.Comment: accepted by ASPDAC 202
NUMERICAL INVESTIGATION OF GAS SAMPLING FROM FLUIDIZED BEDS
Gas mixing in a tall narrow fluidized bed operated in the slugging fluidization regime is studied with the aid of computational fluid dynamics. Three-dimensional numerical simulations are performed with an Eulerian-Eulerian model. Predicted axial and radial tracer concentration profiles for various operating conditions are generally in good agreement with experimental data from the literature. Different field variables including voidage, tracer concentration, and gas velocity at upstream and downstream levels are analysed to study gas mixing. Mean tracer concentrations in the dense phase and the bubble phase are evaluated and significant differences between them are found. The time-mean concentration is weighted heavily towards the dense phase concentration which may lead to misinterpretation of sampling data in dispersion models. Caution is needed when interpreting time-mean tracer concentration data. A flux-based mean tracer concentration is introduced to characterize the gas mixing in numerical simulations of two-phase fluidized beds
Class-based Quantization for Neural Networks
In deep neural networks (DNNs), there are a huge number of weights and
multiply-and-accumulate (MAC) operations. Accordingly, it is challenging to
apply DNNs on resource-constrained platforms, e.g., mobile phones. Quantization
is a method to reduce the size and the computational complexity of DNNs.
Existing quantization methods either require hardware overhead to achieve a
non-uniform quantization or focus on model-wise and layer-wise uniform
quantization, which are not as fine-grained as filter-wise quantization. In
this paper, we propose a class-based quantization method to determine the
minimum number of quantization bits for each filter or neuron in DNNs
individually. In the proposed method, the importance score of each filter or
neuron with respect to the number of classes in the dataset is first evaluated.
The larger the score is, the more important the filter or neuron is and thus
the larger the number of quantization bits should be. Afterwards, a search
algorithm is adopted to exploit the different importance of filters and neurons
to determine the number of quantization bits of each filter or neuron.
Experimental results demonstrate that the proposed method can maintain the
inference accuracy with low bit-width quantization. Given the same number of
quantization bits, the proposed method can also achieve a better inference
accuracy than the existing methods.Comment: accepted by DATE2023 (Design, Automation and Test in Europe
CorrectNet: Robustness Enhancement of Analog In-Memory Computing for Neural Networks by Error Suppression and Compensation
The last decade has witnessed the breakthrough of deep neural networks (DNNs)
in many fields. With the increasing depth of DNNs, hundreds of millions of
multiply-and-accumulate (MAC) operations need to be executed. To accelerate
such operations efficiently, analog in-memory computing platforms based on
emerging devices, e.g., resistive RAM (RRAM), have been introduced. These
acceleration platforms rely on analog properties of the devices and thus suffer
from process variations and noise. Consequently, weights in neural networks
configured into these platforms can deviate from the expected values, which may
lead to feature errors and a significant degradation of inference accuracy. To
address this issue, in this paper, we propose a framework to enhance the
robustness of neural networks under variations and noise. First, a modified
Lipschitz constant regularization is proposed during neural network training to
suppress the amplification of errors propagated through network layers.
Afterwards, error compensation is introduced at necessary locations determined
by reinforcement learning to rescue the feature maps with remaining errors.
Experimental results demonstrate that inference accuracy of neural networks can
be recovered from as low as 1.69% under variations and noise back to more than
95% of their original accuracy, while the training and hardware cost are
negligible.Comment: Accepted by DATE 2023 (Design, Automation and Test in Europe
Text mining and sentiment analysis of COVID-19 tweets
The human severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2),
causing the COVID-19 disease, has continued to spread all over the world. It
menacingly affects not only public health and global economics but also mental
health and mood. While the impact of the COVID-19 pandemic has been widely
studied, relatively fewer discussions about the sentimental reaction of the
population have been available. In this article, we scrape COVID-19 related
tweets on the microblogging platform, Twitter, and examine the tweets from
Feb~24, 2020 to Oct~14, 2020 in four Canadian cities (Toronto, Montreal,
Vancouver, and Calgary) and four U.S. cities (New York, Los Angeles, Chicago,
and Seattle). Applying the Vader and NRC approaches, we evaluate the sentiment
intensity scores and visualize the information over different periods of the
pandemic. Sentiment scores for the tweets concerning three anti-epidemic
measures, masks, vaccine, and lockdown, are computed for comparisons. The
results of four Canadian cities are compared with four cities in the United
States. We study the causal relationships between the infected cases, the tweet
activities, and the sentiment scores of COVID-19 related tweets, by integrating
the echo state network method with convergent cross-mapping. Our analysis shows
that public sentiments regarding COVID-19 vary in different time periods and
locations. In general, people have a positive mood about COVID-19 and masks,
but negative in the topics of vaccine and lockdown. The causal inference shows
that the sentiment influences people's activities on Twitter, which is also
correlated to the daily number of infections.Comment: 20 pages, 10 figures, 1 tabl
Expressivity Enhancement with Efficient Quadratic Neurons for Convolutional Neural Networks
Convolutional neural networks (CNNs) have been successfully applied in a
range of fields such as image classification and object segmentation. To
improve their expressivity, various techniques, such as novel CNN
architectures, have been explored. However, the performance gain from such
techniques tends to diminish. To address this challenge, many researchers have
shifted their focus to increasing the non-linearity of neurons, the fundamental
building blocks of neural networks, to enhance the network expressivity.
Nevertheless, most of these approaches incur a large number of parameters and
thus formidable computation cost inevitably, impairing their efficiency to be
deployed in practice. In this work, an efficient quadratic neuron structure is
proposed to preserve the non-linearity with only negligible parameter and
computation cost overhead. The proposed quadratic neuron can maximize the
utilization of second-order computation information to improve the network
performance. The experimental results have demonstrated that the proposed
quadratic neuron can achieve a higher accuracy and a better computation
efficiency in classification tasks compared with both linear neurons and
non-linear neurons from previous works
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