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07/18/2023
Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference
Analog In-Memory Computing (AIMC) is a promising approach to reduce the ...
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05/26/2023
Gradient descent-based programming of analog in-memory computing cores
The precise programming of crossbar arrays of unit-cells is crucial for ...
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05/17/2023
AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing
The advancement of Deep Learning (DL) is driven by efficient Deep Neural...
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02/16/2023
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
Analog in-memory computing (AIMC) – a promising approach for energy-effi...
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07/14/2022
In-memory Realization of In-situ Few-shot Continual Learning with a Dynamically Evolving Explicit Memory
Continually learning new classes from a few training examples without fo...
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04/13/2022
Joint Coreset Construction and Quantization for Distributed Machine Learning
Coresets are small, weighted summaries of larger datasets, aiming at pro...
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04/05/2021
A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrays
We introduce the IBM Analog Hardware Acceleration Kit, a new and first o...
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05/05/2020
Nanotechnology-inspired Information Processing Systems of the Future
Nanoscale semiconductor technology has been a key enabler of the computi...
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04/11/2019