Virtual Coset Coding for Encrypted Non-Volatile Memories with Multi-Level Cells

Recently, Phase-Change Memory (PCM) has become a popular commercialized non-volatile memory (NVM), which has been deployed as a backing memory for DRAM main memory, secondary storage, or even as a DRAM main memory replacement. Like other NVMs, PCM has asymmetric access energy; writes dominate reads. When considering multilevel cells (MLC), this asymmetry can vary by an order of magnitude. Many schemes have been developed to take advantage of the asymmetric patterns of ‘0’s and ‘1’s in the data to reduce write energy. Because the memory is non-volatile, data can be recovered via physical attack or across system reboot cycles. To protect information stored in PCM against these attacks requires encryption. Unfortunately, most encryption algorithms scramble ‘0’s and ‘1’s in the data, effectively removing any patterns and negatively impacting schemes that leverage data bias and similarity to reduce write energy. In this paper, we introduce Virtual Coset Coding (VCC) as a workload-independent approach that reduces costly symbol transitions for storing encrypted data. VCC is based on two ideas. First, using coset encoding with random coset candidates, it is possible to effectively reduce the frequency of costly bit/symbol transitions when writing encrypted data. Second, a small set of random substrings can be used to achieve the same encoding efficiency as a large number of random coset candidates, but at a much lower encoding/decoding cost. Additionally, we demonstrate how VCC can be leveraged for energy reduction in combination with fault-mitigation and fault-tolerance to dramatically increase the lifetimes of endurance-limited NVMs, such as PCM. We evaluate the design of VCC and demonstrate that it can be implemented on-chip with only a nominal area overhead. VCC reduces dynamic energy by 22-28% while maintaining the same performance. Using our multi-objective optimization approach achieves at least a 36% improvement in lifetime over the state-of-the-art and at least a 50% improvement in lifetime vs. an unencoded memory, while maintaining its energy savings and system performance.