In the hardware implementation of deep learning algorithms such as, convolutional neural networks (CNNs) and binarized neural networks (BNNs), multiple dot products and memories for storing parameters take a significant portion of area and power consumption. In this paper, we propose a domain wall memory (DWM) based design of CNN and BNN convolutional layers. In the proposed design, the resistive cell sensing mechanism is efficiently exploited to design low-cost DWM-based cell arrays for storing parameters. The unique serial access mechanism and small footprint of DWM are also used to reduce the area and energy cost of DWM-based design for filter sliding. Simulation results with 65 nm CMOS process show 45% and 43% of energy savings compared to the conventional CNN and BNN design approach, respectively.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Materials Science(all)