FS-07 On-Chip Learning of UNet Architecture Using Spintronic Devices for Image Segmentation
Creators: Venkatesh Vadde
Bhaskaran Muralidharan
Abhishek Sharma
Publisher: name: IEEE
Year: 2025

Image segmentation is a crucial machine-learning task that involves classifying and clustering each pixel in an image to identify objects. Segmentation has substantial uses in self-driving cars, analyzing medical and satellite images [1]. UNet has become the prominent architecture for segmentation tasks. In this work, we design spintronic hardware for various layers of the UNet architecture, including convolution, deconvolution, activation, and pooling functions. We implement these layers by employing a hybrid of orthogonal current-injected magnetic tunnel junction (MTJ) and domain wall MTJs (DW-MTJ). Leveraging the linear behavior [2] of orthogonal current-injected MTJs, we demonstrate the implementation of the rectified linear unit (ReLU) activation function and the max-pooling function. By utilizing the synaptic properties of DW-MTJs, we achieve the realization of convolution and deconvolution operations [3]. We simulate these devices using a hybrid simulation platform that couples micromagnetic simulations, the Landau-Lifshitz-Gilbert-Slonczewski (LLGS) equations, non-equilibrium Green’s function, SPICE circuit simulation, and machine learning network implementation. We demonstrate the efficiency of our proposed network by testing it on the Cambridge-driving labeled video (CamVid) dataset, achieving an accuracy of 83.71%, comparable to that of the software implementation. Furthermore, we demonstrate that by utilizing unstable ferromagnets, the network's energy consumption can be reduced by up to an order of magnitude, decreasing from 821 mJ to 85.79 mJ.References: [1] Liu, X., Deng, Z., & Yang, Y. Artificial Intelligence Review, 52, 1089-1106, (2019). [2] Vadde, V., Muralidharan, B., & Sharma, A. IEEE Transactions on Electron Devices, 70(7), 3943-3950, (2023). [3] Vadde, V., Muralidharan, B., & Sharma, A. APL Machine Learning, 2(3), 036101 (2024).
Image segmentation is a crucial machine-learning task that involves classifying and clustering each pixel in an image to identify objects. Segmentation has substantial uses in self-driving cars, analyzing medical and satellite images [1]. UNet has become the prominent architecture for segmentation tasks. In this work, we design spintronic hardware for various layers of the UNet architecture, including convolution, deconvolution, activation, and pooling functions. We implement these layers by employing a hybrid of orthogonal current-injected magnetic tunnel junction (MTJ) and domain wall MTJs (DW-MTJ). Leveraging the linear behavior [2] of orthogonal current-injected MTJs, we demonstrate the implementation of the rectified linear unit (ReLU) activation function and the max-pooling function. By utilizing the synaptic properties of DW-MTJs, we achieve the realization of convolution and deconvolution operations [3]. We simulate these devices using a hybrid simulation platform that couples micromagnetic simulations, the Landau-Lifshitz-Gilbert-Slonczewski (LLGS) equations, non-equilibrium Green’s function, SPICE circuit simulation, and machine learning network implementation. We demonstrate the efficiency of our proposed network by testing it on the Cambridge-driving labeled video (CamVid) dataset, achieving an accuracy of 83.71%, comparable to that of the software implementation. Furthermore, we demonstrate that by utilizing unstable ferromagnets, the network's energy consumption can be reduced by up to an order of magnitude, decreasing from 821 mJ to 85.79 mJ.References: [1] Liu, X., Deng, Z., & Yang, Y. Artificial Intelligence Review, 52, 1089-1106, (2019). [2] Vadde, V., Muralidharan, B., & Sharma, A. IEEE Transactions on Electron Devices, 70(7), 3943-3950, (2023). [3] Vadde, V., Muralidharan, B., & Sharma, A. APL Machine Learning, 2(3), 036101 (2024).