  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.
