OMT: A Demand-Adaptive, Hardware-Targeted Bonsai Merkle Tree Framework for Embedded Heterogeneous Memory Platform.

Novel flash-based, crash-tolerant, non-volatile memory (NVM) such as Intel's Optane DC memory brings about new and exciting use-case scenarios for both traditional and embedded computing systems involving Field-Programmable Gate Arrays (FPGA). However, NVMs cannot be proper replacement for existing DDR memory modules due to low write endurance and are more well-suited for a hybrid NVM + Volatile memory system. They are also well-known to be vulnerable to different memory-based adversaries that demand the use of a robust authentication method such as Bonsai Merkle Tree. However, typical update process of a BMT (eager update) requires updating the entire update chain frequently, affecting run-time performance even for the data that is not persistence-critical. The latest intermittent BMT update techniques can help provide better real-time throughput, but they lack crash-consistency. A heterogeneous memory-based system would, therefore, greatly benefit from an authentication mechanism that can change its update method on-the-fly. Hence we propose a modular, unified and adaptable hardware-based BMT framework called Opportunistic Merkle tree (OMT). OMT combines two BMT with different update methods and streamlines the BMT read with a common datapath to provide support for both recovery-critical and general data, eliminating the need for individual authentication subsystems for heterogeneous memory platforms. It also allows for a switch between the update methods based on the request type (persistent/intermittent) while considerably reducing the resource overhead compared to standalone BMT implementations. We test OMT on a heterogeneous embedded secure memory system and the setup provides 44% lower memory overhead & up to 22% faster execution in synthetic benchmarks compared to a baseline.