## ++++ 2017 [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://arxiv.org/abs/1703.09039) Vivienne Sze

- 2017 [Hardware for Machine Learning: Challenges and Opportunities](https://arxiv.org/pdf/1612.07625.pdf) (Invited Paper) Vivienne Sze

- [Data movement - In order to meet computing demands in terms of power and speed, need to redesign computing hardware from the ground up](https://knowen-production.s3.amazonaws.com/uploads/attachment/file/3320/2019-mars-sze-compressed.pdf) Vivienne Sze - technical talk

- [A Microprocessor implemented in 65nm CMOS with Configurable and Bit-scalable Accelerator for Programmable In-memory Computing](https://arxiv.org/ftp/arxiv/papers/1811/1811.04047.pdf) Naveen Verma

## https://www.intrinsicsemi.com/

[Memristors--from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing](https://arxiv.org/ftp/arxiv/papers/2004/2004.14942.pdf)
A Mehonic, A Sebastian, B Rajendran, O Simeone, E Vasilaki, AJ Kenyon

[UCL spun-out Intrinsic closes £1.35M seed funding to develop next-gen memory devices](https://www.uktech.news/ai/ucl-spin-out-intrinsic-closes-1-35m-seed-funding-to-develop-next-gen-memory-devices-20210322)

 
## +++ 2020 [In‐Memory Vector‐Matrix Multiplication in Monolithic Complementary Metal–Oxide–Semiconductor‐Memristor Integrated Circuits: Design Choices, Challenges, and Perspectives](https://onlinelibrary.wiley.com/doi/full/10.1002/aisy.202000115)

ReRam based .... frequently analog-based

**flexibility versus energy efficiency**
write a summary

2020 [In‐Memory Binary Vector–Matrix Multiplication Based on Complementary Resistive Switches](https://onlinelibrary.wiley.com/doi/full/10.1002/aisy.202000134)