Neural networks over conventional computing platforms are heavily restricted by the data volume and performance concerns. While non-volatile memory offers potential solutions to data volume issues, challenges must be faced over performance issues, especially with asymmetric read and write performance. Beside that, critical concerns over endurance must also be resolved before non-volatile memory could be used in reality for neural networks. This work addresses the performance and endurance concerns altogether by proposing a data-aware programming scheme. We propose to consider neural network training jointly with respect to the data-flow and data-content points of view. In particular, methodologies with approximate results over Dual-SET operations were presented. Encouraging results were observed through a series of experiments, where great efficiency and lifetime enhancement is seen without sacrificing the result accuracy.
All Science Journal Classification (ASJC) codes
- Hardware and Architecture