deep learning
(i) Recovery of Deep Learning Performance of a degraded array - To examine the practical applications of adaptive oxide-based memristors in deep learning, I collaborated with IBM T. J. Watson Research Center. We demonstrate ReSta—a novel biasing technique. Designed to rejuvenate the filaments of fatigued memristive arrays after repeated training, ReSta significantly boosts long-term neural network accuracy on analog AI hardware. Next, we leveraged open loop training without write verification on this hardware with 98% accuracy. [IEEE Transactions on Electron Devices]
(ii) Demonstration of Transfer Learning in a 14 nm Technology chip - We also demonstrated efficient deep neural network transfer learning utilizing hardware and algorithm co-optimization in an analog HfOx based ReRAM array. We demonstrate that in deep neural network (DNN) transfer learning for image classification tasks, convergence rates can be accelerated by approximately 3.5 times by utilizing co-optimized analog ReRAM hardware and hardware-aware Tiki-Taka v2 algorithm. [Frontiers in Electronics]
(ii) Demonstration of Transfer Learning in a 14 nm Technology chip - We also demonstrated efficient deep neural network transfer learning utilizing hardware and algorithm co-optimization in an analog HfOx based ReRAM array. We demonstrate that in deep neural network (DNN) transfer learning for image classification tasks, convergence rates can be accelerated by approximately 3.5 times by utilizing co-optimized analog ReRAM hardware and hardware-aware Tiki-Taka v2 algorithm. [Frontiers in Electronics]
