Presentation
Heterogeneous Approximate Multiplications: A New Frontier for Practical DNNs
DescriptionApproximate multipliers have been used in deep learning and AI models to enhance power efficiency while maintaining acceptable performance. However, a key drawback of this technique is the computational errors that can negatively affect the model accuracy. In this study, we propose a novel method for using approximate multipliers in deep learning models that not only improves power efficiency but also enhances accuracy. Our proposed method involves a layer-wise heterogeneous approach for quantized approximate multipliers (INT8) in Deep Neural Network (DNN) models (Convolutional Neural Network (CNN), Multilayer Perceptron (MLP), VGG11, and VGG13) with different levels of approximation across layers. Thanks to our method, we achieve average energy savings of approximately 70% for VGG11 and 67% for VGG13, while surpassing trained Top-1 accuracy by up to 2.07% in all case studies. Delay improvements were also notable, with VGG11 achieving up to 37% and VGG13 up to 34%. Our method, which can be considered a novel tuning or retraining approach, reduces the required number of MAC operations during retraining by 146× to 1125× compared to traditional retraining methods, introducing a new level of computational efficiency and power saving, beyond those of inference mentioned above. This study showcases the potential of heterogeneous approximate multipliers and our novel retraining methods to advance efficiency and performance of creating and using new deep learning models, taking a step towards energy-conscious artificial intelligence
Event Type
Networking
Work-in-Progress Poster
TimeMonday, June 236:00pm - 7:00pm PDT
LocationLevel 2 Lobby