K. Spoon

Enabling High-Performance DNN Inference Accelerators Using Non-Volatile Analog Memory (Invited)

Pritish Narayanan, Geoffrey W. Burr, Stefano Ambrogio, 2020, 2020 4th IEEE Electron Devices Technology & Manufacturing Conference (EDTM).

Fully on-chip MAC at 14nm enabled by accurate row-wise programming of PCM-based weights and parallel vector-transport in duration-format

P. Narayanan, I. Ahsan, G. Burr, 2021, 2021 Symposium on VLSI Technology.

Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices

Pritish Narayanan, Stefano Ambrogio, Hsinyu Tsai, 2021, Frontiers in Computational Neuroscience.

Reducing the Impact of Phase-Change Memory Conductance Drift on the Inference of large-scale Hardware Neural Networks

S. Ambrogio, A. Kumar, A. Chen, 2019, 2019 IEEE International Electron Devices Meeting (IEDM).

Towards Detecting Dyslexia in Children's Handwriting Using Neural Networks

David J. Crandall, K. Spoon, K. Siek, 2019 .

Noise-Resilient DNN: Tolerating Noise in PCM-Based AI Accelerators via Noise-Aware Training

Moinuddin K. Qureshi, P. Narayanan, G. Burr, 2021, IEEE Transactions on Electron Devices.

Analog-memory-based 14nm Hardware Accelerator for Dense Deep Neural Networks including Transformers

M. Rasch, P. Narayanan, G. Burr, 2022, International Symposium on Circuits and Systems.

Mushroom-Type phase change memory with projection liner: An array-level demonstration of conductance drift and noise mitigation

S. Ambrogio, A. Sebastian, T. Yamashita, 2021, 2021 IEEE International Reliability Physics Symposium (IRPS).

Fully On-Chip MAC at 14 nm Enabled by Accurate Row-Wise Programming of PCM-Based Weights and Parallel Vector-Transport in Duration-Format

I. Ahsan, A. Friz, W. Haensch, 2021, IEEE Transactions on Electron Devices.