Planting Fast-Growing Forest by Leveraging the Asymmetric Read/Write Latency of NVRAM-Based Systems

Yu Pei Liang, Tseng Yi Chen, Yuan Hao Chang, Yi Da Huang, Wei Kuan Shih

Research output: Contribution to journalArticlepeer-review


Owing to the considerations of cell density and low static power consumption, nonvolatile random-access memory (NVRAM) has been a promising candidate for collaborating with a dynamic random-access memory (DRAM) as the main memory in modern computer systems. As NVRAM also brings technical challenges (e.g., limited endurance and high writing cost) to computer system developers, the concept of write reduction becomes the famous doctrine in NVRAM-based system design. Unfortunately, a well-known machine learning algorithm, random forest, will generate a massive amount of write traffic to the main memory space during its construction phase. In other words, a random forest hits the Achilles' heel of NVRAM-based systems. For remedying this pain, our work proposes an NVRAM-friendly random forest algorithm, namely, Amine, for an NVRAM-based system. The design principle of Amine is to replace write operations with read accesses without raising the read complexity of the random forest algorithm. According to experimental results, Amine can effectively decrease the latency of random forest construction by 64%, compared with the original random forest algorithm.

Original languageEnglish
Pages (from-to)3304-3317
Number of pages14
JournalIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Issue number10
StatePublished - 1 Oct 2022


  • Decision tree
  • nonvolatile memory
  • nonvolatile random-access memory (NVRAM)-based learning
  • random forest


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