TaxoNN: A Light-Weight Accelerator for Deep Neural Network Training

• Main Authors: Hojabr, Reza, Givaki, Kamyar, Pourahmadi, Kossar, Nooralinejad, Parsa, Khonsari, Ahmad, Rahmati, Dara, Najafi, M. Hassan
• Format: Conference Proceeding
• Language: English
• Subjects: Artificial neural networks; Computer architecture; Feature extraction; Hardware; Inference algorithms; Machine learning; Training
• ISSN: 2158-1525; 2158-1525
• DOI: 10.1109/ISCAS45731.2020.9181001

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× power saving and 1.65× area reduction over the state-of-the-art DNN training accelerator.