Spintronic Implementation of UNet for Image Segmentation

Image segmentation plays a crucial role in computer vision applications like self-driving cars, satellite imagery analysis, and medical diagnosis. Implementing these complex deep neural networks on conventional hardware is highly inefficient. In this work, we propose hardware implementation of UNet for segmentation tasks, using spintronic devices. Our approach involves designing hardware for convolution, deconvolution, ReLU, and max pooling layers of the UNet architecture. We demonstrate the synaptic behavior of the domain wall MTJ, and design convolution and deconvolution layers using the domain wall-based crossbar array. We utilize the orthogonal current injected MTJ with its continuous resistance change and showcase the ReLU and max pooling functions. We employ a hybrid simulation setup by coupling micromagnetic simulation, non-equilibrium Green's function, Landau-Lifshitz-Gilbert-Slonczewski equations, and circuit simulation with Python programming to incorporate the diverse physics of spin-transport, magnetization dynamics, and CMOS elements in our proposed designs. We evaluate our UNet design on the CamVid dataset and achieve segmentation accuracies that are comparable to software implementation. During training, our design consumes 43.59pJ of energy for synaptic weight updates.