## 2019 [MLP+NeuroSimV3.0: Improving On-chip Learning Performance with Device to Algorithm Optimizations](https://knowen-production.s3.amazonaws.com/uploads/attachment/file/5352/luo2019.pdf)

+++ georgia tech

- https://shimeng.ece.gatech.edu/downloads/

- 2017 [NeuroSim+: An Integrated Device­to­Algorithm Framework for Benchmarking Synaptic Devices and Array Architectures](https://knowen-production.s3.amazonaws.com/uploads/attachment/file/5353/chen2017.pdf)

## 2017 [Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning](https://arxiv.org/pdf/1611.05128.pdf)
> Tien-Ju Yang, Yu-Hsin Chen, Vivienne Sze


##  https://dfan.engineering.asu.edu/  ... detailed NeuroSim 

- https://dfan.engineering.asu.edu/in-memory-computing/

- [Processing-In-Memory Acceleration of Convolutional Neural Networks for **Energy-Efficiency, and Power-Intermittency Resilience**](https://arxiv.org/pdf/1904.07864.pdf)

- [Deep Neural Network Acceleration in Non-Volatile Memory: A Digital Approach](https://dfan.engineering.asu.edu/wp-content/uploads/2020/04/NanoArc2019_invited.pdf)
Shaahin Angizi and Deliang Fan

> ....In this paper, we review several of our recent works regarding Processing-inMemory (PIM) accelerator based on Magnetic Random Access Memory computational sub-arrays to accelerate the inference
> mode of quantized neural networks **using digital non-volatile memory rather than using analog crossbar operation**

## [Mitigate Parasitic Resistance in Resistive Crossbar-based Convolutional Neural Networks](https://arxiv.org/pdf/1912.08716.pdf) FAN ZHANG, Binghamton University, USA MIAO HU