Praneet Adusumilli

Deep Neural Network,Computational Memory,Drift Coefficient,Phase Change Materials,Synaptic Weights,AI Systems,Accelerator Architecture,Amorphous Phase,Amorphous Solid,Architectural Work,Array Of Devices,Batch Size,Bidirectional Encoder Representations,Closed-loop Feedback,Computational Core,Computer Science,Conductance Drift,Convolutional Neural Network,Cross-entropy Loss,Crystalline Phase,Deep Learning Inference,Deep Neural Network Layers,Deep Neural Network Model,Design Considerations,Digital Computer,Efficiency Improvement,Endurance,Energy Efficiency,Function Of Time,Hidden State,High Energy Efficiency,High Resistance,Impedance,Intermediate State,Iterative Scheme,Long Short-term Memory,Long Short-term Memory Network,Low Drift,Low Noise,Low Reads,Margin Of Error,Matrix Multiplication,Memory Devices,Multicast,Multiple Chips,Multiple Cores,Mushroom-type Phase Change Memory,Neural Network,Noise Control,Non-volatile Memory,