Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Incremental Training and Group Convolution Pruning for Runtime DNN
  Performance Scaling on Heterogeneous Embedded Platforms   Inference for Deep Neural Networks is increasingly being executed locally on
mobile and embedded platforms due to its advantages in latency, privacy and
connectivity. Since modern System on Chips typically execute a combination of
different and dynamic workloads concurrently, it is challenging to consistently
meet inference time/energy budget at runtime because of the local computing
resources available to the DNNs vary considerably. To address this challenge, a
variety of dynamic DNNs were proposed. However, these works have significant
memory overhead, limited runtime recoverable compression rate and narrow
dynamic ranges of performance scaling. In this paper, we present a dynamic DNN
using incremental training and group convolution pruning. The channels of the
DNN convolution layer are divided into groups, which are then trained
incrementally. At runtime, following groups can be pruned for inference
time/energy reduction or added back for accuracy recovery without model
retraining. In addition, we combine task mapping and Dynamic Voltage Frequency
Scaling (DVFS) with our dynamic DNN to deliver finer trade-off between accuracy
and time/power/energy over a wider dynamic range. We illustrate the approach by
modifying AlexNet for the CIFAR10 image dataset and evaluate our work on two
heterogeneous hardware platforms: Odroid XU3 (ARM big.LITTLE CPUs) and Nvidia
Jetson Nano (CPU and GPU). Compared to the existing works, our approach can
provide up to 2.36x (energy) and 2.73x (time) wider dynamic range with a 2.4x
smaller memory footprint at the same compression rate. It achieved 10.6x
(energy) and 41.6x (time) wider dynamic range by combining with task mapping
and DVFS.

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The categories of this paper are: hep-th
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The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
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The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: plasm-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: econ
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The categories of this paper are: hep-ph
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The categories of this paper are: funct-an
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The categories of this paper are: acc-phys