Performance evaluation artefacts for in-memory encryption using the advanced encryption standard

Encryption and decryption of data with very low latency and high energy efficiency is desirable in almost every application that deals with sensitive data. The Advanced Encryption Standard (AES) is a widely adopted algorithm in symmetric key cryptography with numerous efficient implementations. Nonetheless, in scenarios involving extensive data processing, the primary limitations on performance and efficiency arise from data movement between memory and the processor, rather than data processing itself. In this paper, we present a novel in-memory computing (IMC) approach for AES encryption and key-expansion, and experimentally validate it on an IMC prototype chip based on phase-change memory (PCM) technology. We leverage operators stored in PCM crossbar arrays to achieve the flexibility to tune performance at runtime based on the amount of free storage available in the memory system. Additionally, we introduce a method for parallel in-memory polynomial modular multiplication and evaluate..., The dataset includes benchmarking code in order to reproduce the performance evaluations in the paper. Please refer to the README.md for further details., , # In-memory encryption using the advanced encryption standard artifacts

This repository includes the code to reproduce the performance plots from the paper available at [https://doi.org/10.1098/rsta.2023.0396](https://doi.org/10.1098/rsta.2023.0396).

## Reproducing the results

### Using Docker

We recommend using docker:

```
docker build .
```

will load all dependencies, compile the model, run the performance benchmarks and store the resulting plots in the **plots/** directory of the conainer.

### **Manual**

First install the system dependencies:

```
apt-get update && apt-get install -y curl gnupg apt-utils && \
apt-get install -y apt-transport-https curl gnupg git perl python3 pip make g++ pkg-config curl zip unzip tar
```

Then run:

```
./setup.sh
```

to set up the environment, install python modules and compile the simulator.
Finally, to obtain the performance plots, run:

```
python3 eval_performance.py
```

## **Directory structure**

* **arch_model/** contains the so...