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 potential of intrinsic stochastic properties of PCM devices for random key generation. We show how to further improve efficiency with minimal additional auxiliary circuitry. To evaluate the performance within a custom-built large-scale in-memory AES system, we design and implement a cycle-accurate simulator that integrates parameters from Spice simulations for detailed latency and energy consumption analysis of the AES algorithm. Our evaluations indicate that our IMC-based AES approach outperforms state-of-the- art methods, achieving speed improvements of up to 19.7× at equivalent energy efficiency.

The dataset includes benchmarking code in order to reproduce the performance evaluations in the paper. Please refer to the README.md for further details.