28 research outputs found
Pesticide effects on body temperature of torpid/hibernating rodents (Peromyscus leucopus and Spermophilus tridecemlineatus)
Environmental contaminants have been shown in the lab to alter thyroid hormone concentrations. Despite the role these hormones play in the physiological ecology of small mammals, no one has investigated the possible
effects of thyroid-disrupting chemicals on mammalian thermal ecology and thermoregulatory ability. Because the energetic impact of such a disruption is likely to be most dramatic during times already energetically stressful, we investigated the effects of two common pesticides (atrazine and lindane) on the use of daily torpor in white-footed mice, and the use of hibernation in 13-lined ground squirrels. Fortunately, we found that these strategies for over-wintering success were not impaired
DLBricks: Composable Benchmark Generation to Reduce Deep Learning Benchmarking Effort on CPUs (Extended)
The past few years have seen a surge of applying Deep Learning (DL) models
for a wide array of tasks such as image classification, object detection,
machine translation, etc. While DL models provide an opportunity to solve
otherwise intractable tasks, their adoption relies on them being optimized to
meet latency and resource requirements. Benchmarking is a key step in this
process but has been hampered in part due to the lack of representative and
up-to-date benchmarking suites. This is exacerbated by the fast-evolving pace
of DL models.
This paper proposes DLBricks, a composable benchmark generation design that
reduces the effort of developing, maintaining, and running DL benchmarks on
CPUs. DLBricks decomposes DL models into a set of unique runnable networks and
constructs the original model's performance using the performance of the
generated benchmarks. DLBricks leverages two key observations: DL layers are
the performance building blocks of DL models and layers are extensively
repeated within and across DL models. Since benchmarks are generated
automatically and the benchmarking time is minimized, DLBricks can keep
up-to-date with the latest proposed models, relieving the pressure of selecting
representative DL models. Moreover, DLBricks allows users to represent
proprietary models within benchmark suites. We evaluate DLBricks using
MXNet models spanning DL tasks on representative CPU systems. We show
that DLBricks provides an accurate performance estimate for the DL models and
reduces the benchmarking time across systems (e.g. within accuracy and
up to benchmarking time speedup on Amazon EC2 c5.xlarge)
Neural Architecture Search: Insights from 1000 Papers
In the past decade, advances in deep learning have resulted in breakthroughs
in a variety of areas, including computer vision, natural language
understanding, speech recognition, and reinforcement learning. Specialized,
high-performing neural architectures are crucial to the success of deep
learning in these areas. Neural architecture search (NAS), the process of
automating the design of neural architectures for a given task, is an
inevitable next step in automating machine learning and has already outpaced
the best human-designed architectures on many tasks. In the past few years,
research in NAS has been progressing rapidly, with over 1000 papers released
since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized
and comprehensive guide to neural architecture search. We give a taxonomy of
search spaces, algorithms, and speedup techniques, and we discuss resources
such as benchmarks, best practices, other surveys, and open-source libraries