analog AI at IBM
inspiring review Analog architectures for neural network acceleration based on non-volatile memory
the challenges are the overhead imposed by peripheral circuitry and non-ideal properties of neuron cells.
https://www.ibm.com/blogs/research/2020/12/iedm2020-memory-analog-ai/
Run an AI algorithm where computation and storage coexist on a single chip
IBM - Analog AI
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dec 2020 Precision of synaptic weights programmed in phase-change memory devices for deep learning inference Date of Conference: 12-18 Dec. 2020
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may 2020 Accurate deep neural network inference using computational phase-change memory - Vinay Joshi
Analog In-Memory Computing using Resistive Processing Unit (RPU) has been proposed for Neural Network (NN) training.
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video SDC2020: Analog Memory-based Techniques for Accelerating Deep Neural Networks
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presentation 2019 Accelerating Deep Learning with Analog Memory - A Device, Circuit and Systems Approach and the full article
sept 2020 - Analog Memory-Based Techniques for Accelerating Deep Neural Networks
Authors: Tayfun Gokmen, Wilfried Haensch
- The Next Generation of Deep Learning Hardware: Analog Computing
This paper explores the current state of neuromorphic deep learning architectures in silicon CMOS technology. By WILFRIED HAENSCH
news
https://www.ibm.com/blogs/research/2020/12/iedm2020-memory-analog-ai/
https://www.ibm.com/blogs/research/2018/07/capacitor-ai-accelerators/