Kovats Tobias, Rameshan Navaneeth, Karunaratne Kumudu Geethan, Giannopoulos Iason, Sebastian Abu
IBM Research-Europe, 8803 Rüschlikon, Zurich, Switzerland.
Philos Trans A Math Phys Eng Sci. 2025 Jan;383(2288):20230396. doi: 10.1098/rsta.2023.0396. Epub 2025 Jan 16.
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 article, 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. In addition, 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 factor improvements of up to 19.7 at equivalent energy efficiency.This article is part of the theme issue 'Emerging technologies for future secure computing platforms'.
在几乎每一个处理敏感数据的应用中,都需要对数据进行具有极低延迟和高能效的加密和解密。高级加密标准(AES)是对称密钥密码学中一种被广泛采用的算法,有众多高效的实现方式。然而,在涉及大量数据处理的场景中,性能和效率的主要限制来自内存与处理器之间的数据移动,而非数据处理本身。在本文中,我们提出了一种用于AES加密和密钥扩展的新型内存计算(IMC)方法,并在基于相变存储器(PCM)技术的IMC原型芯片上进行了实验验证。我们利用存储在PCM交叉阵列中的算子,以实现根据内存系统中可用的空闲存储量在运行时调整性能的灵活性。此外,我们引入了一种用于并行内存多项式模乘的方法,并评估了PCM器件固有随机特性用于生成随机密钥的潜力。我们展示了如何用最少的额外辅助电路进一步提高效率。为了评估在定制构建的大规模内存AES系统中的性能,我们设计并实现了一个周期精确模拟器,该模拟器集成了来自Spice模拟的参数,用于对AES算法进行详细的延迟和能耗分析。我们的评估表明,我们基于IMC的AES方法优于现有技术方法,在同等能效下实现了高达19.7的速度因子提升。本文是主题为“未来安全计算平台的新兴技术”的一部分。