Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: TaxoNN: A Light-Weight Accelerator for Deep Neural Network Training   Emerging intelligent embedded devices rely on Deep Neural Networks (DNNs) to
be able to interact with the real-world environment. This interaction comes
with the ability to retrain DNNs, since environmental conditions change
continuously in time. Stochastic Gradient Descent (SGD) is a widely used
algorithm to train DNNs by optimizing the parameters over the training data
iteratively. In this work, first we present a novel approach to add the
training ability to a baseline DNN accelerator (inference only) by splitting
the SGD algorithm into simple computational elements. Then, based on this
heuristic approach we propose TaxoNN, a light-weight accelerator for DNN
training. TaxoNN can easily tune the DNN weights by reusing the hardware
resources used in the inference process using a time-multiplexing approach and
low-bitwidth units. Our experimental results show that TaxoNN delivers, on
average, 0.97% higher misclassification rate compared to a full-precision
implementation. Moreover, TaxoNN provides 2.1$\times$ power saving and
1.65$\times$ area reduction over the state-of-the-art DNN training accelerator.

31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol