## inspiring review [Analog architectures for neural network acceleration based on non-volatile memory](https://knowen-production.s3.amazonaws.com/uploads/attachment/file/5051/1.5143815.pdf)

>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](https://analog-ai-demo.mybluemix.net/)

## [IBM - Analog AI](https://researcher.watson.ibm.com/researcher/view_group.php?id=7716)

- 2020 [**Unassisted True Analog Neural Network Training Chip**](https://ieeexplore.ieee.org/document/9371973)

- dec 2020 [Precision of synaptic weights programmed in phase-change memory devices for deep learning inference](https://ieeexplore.ieee.org/document/9371990/authors#authors) Date of Conference: 12-18 Dec. 2020

- may 2020 [Accurate deep neural network inference using computational phase-change memory - Vinay Joshi](https://www.nature.com/articles/s41467-020-16108-9)

> Analog In-Memory Computing using Resistive Processing Unit (RPU) has been proposed for Neural Network (NN) training.

- video [SDC2020: Analog Memory-based Techniques for Accelerating Deep Neural Networks](https://www.youtube.com/watch?v=fERwXXdwSM0)

- presentation 2019 [Accelerating Deep Learning with Analog Memory - A Device, Circuit and Systems Approach](https://adacenter.org/sites/default/files/publications/20190602_InMemoryComputingWorkshop_PritishNarayanan.pdf) and [**the full article**](https://knowen-production.s3.amazonaws.com/uploads/attachment/file/5085/pcm%2Bfor%2Bdeep%2Blearning.pdf)

[sept 2020 - Analog Memory-Based Techniques for Accelerating Deep Neural Networks](https://knowen-production.s3.amazonaws.com/uploads/attachment/file/5087/149-Tsai-Analog-Memory-Based-Techniques.pdf)

[Authors: Tayfun Gokmen, Wilfried Haensch](https://arxiv.org/search/cs?searchtype=author&query=Gokmen%2C+T)

- [The Next Generation of Deep Learning Hardware: Analog Computing](https://knowen-production.s3.amazonaws.com/uploads/attachment/file/5086/haensch2019.pdf)
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/