Power Side-Channel Attacks and Countermeasures on Computation-in-Memory Architectures and Technologies

Abstract

To overcome the bottleneck of the classical processor-centric architectures, Computation-in-Memory (CiM) is a promising paradigm where operations are performed directly in memory. Recent works propose the use of CiM to accelerate neural networks or hyperdimensional computing, but also for memory encryption solutions. As CiM facilitates the computation in the analog domain and the output is driven through current sensing, CiM could potentially be highly vulnerable to power side-channel attacks. In this work, we analyze the vulnerability for power side-channel attacks in various CiM implementations based on Static Random Access Memory (SRAM) and emerging nonvolatile memristive technologies. Our results show that a side-channel attacker can recover secret data used in an XOR operation with only a few hundred measurements, where CiM architectures based on emerging memristive technologies are more vulnerable than SRAM-based CiM. Therefore, we propose two different types of countermeasures based on hiding and masking, which are tailored to CiM architectures. The efficiency of our proposed countermeasures is shown by both attacks and leakage assessment methodologies using one million measurement traces.