The paper proposes two OS-supported memory classes, LtRAM for long-lived read-heavy data and StRAM for ephemeral hot data, as a response to the claimed end of SRAM and DRAM cost scaling.
Additionally accepted for presentation in NeurIPS 2025 Workshop: Interpreting Cognition in Deep Learning Models (CogInterp)
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Towards Memory Specialization: A Case for Long-Term and Short-Term RAM
The paper proposes two OS-supported memory classes, LtRAM for long-lived read-heavy data and StRAM for ephemeral hot data, as a response to the claimed end of SRAM and DRAM cost scaling.