AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing
- Hadjer Benmeziane
- Corey Lammie
- et al.
- 2023
- EDGE 2023
Publications
This is our catalog of publications authored by IBM researchers, in collaboration with the global research community. It’s an ever-growing body of work that shows why IBM is one of the most important contributors to modern computing.
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20 results for Malte J. Rasch
Deep learning acceleration in 14nm CMOS compatible ReRAM array: device, material and algorithm co-optimization
- N. Gong
- Malte J. Rasch
- et al.
- 2022
- IEDM 2022
Precision of synaptic weights programmed in phase-change memory devices for deep learning inference
- S. R. Nandakumar
- Irem Boybat
- et al.
- 2020
- IEDM 2020
On-Chip Trainable 1.4M 6T2R PCM Synaptic Array with 1.6K Stochastic LIF Neurons for Spiking RBM
- Masatoshi Ishii
- U. Shin
- et al.
- 2019
- IEDM 2019
The marriage of training and inference for scaled deep learning analog hardware
- Tayfun Gokmen
- Malte J. Rasch
- et al.
- 2019
- IEDM 2019
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
- Malte J. Rasch
- Charles Mackin
- et al.
- 2023
- Nature Communications
Optimization of Projected Phase Change Memory for Analog In-Memory Computing Inference
- Ning Li
- Charles Mackin
- et al.
- 2023
- Advanced Materials
Optimised weight programming for analogue memory-based deep neural networks
- Charles Mackin
- Malte J. Rasch
- et al.
- 2022
- Nature Communications
A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference
- Manuel Le Gallo
- Riduan Khaddam-Aljameh
- et al.
- 2023
- Nature Electronics
Resistive Memory Process Optimization for High Resistance Switching Toward Scalable Analog Compute Technology for Deep Learning
- Youngseok Kim
- Soon-Cheon Seo
- et al.
- 2021
- IEEE Electron Device Letters