GainSight — data lifetime profiling for heterogeneous on-chip memory
GainSight extracts cycle-accurate data lifetimes from accelerator workloads and correlates them against emerging memory device characteristics to generate optimal heterogeneous on-chip memory compositions. Retargetable simulator backends feed an architecture-agnostic analytical frontend, so the same analysis applies across very different accelerator shapes. I have been the first author and lead developer since 2024.
How it works
- Backends. Accel-Sim for NVIDIA GPU modeling and SCALE-Sim for systolic arrays.
- Workloads. MLPerf Inference and PolyBench.
- Frontend. An architecture-agnostic analytical stage that correlates measured lifetime distributions with device retention properties and solves for a memory composition.
What it found
Across MLPerf Inference and PolyBench, 64.3% of first-level GPU cache accesses and 79% of systolic-array scratchpad accesses have sub-microsecond lifetimes. Memory that is provisioned to hold data for milliseconds is therefore overprovisioned in retention for most of what it stores. Composing the on-chip hierarchy from denser, shorter-retention devices instead reaches up to 3× active energy and 4× area reduction against a uniform SRAM baseline.
Where it has been used
- The gain-cell memory compiler study described in OpenGCRAM adopts GainSight as its evaluation methodology, using it to extract the L1 and L2 cache performance demands of the AI workloads it evaluates.
- The argument in Towards Memory Specialization (DIMES ‘25) rests on the lifetime characterization GainSight produced.
- An undergraduate researcher I mentored in summer 2025 built a trace pipeline that feeds directly into the GainSight workflow, extending a commercial toolchain that had no cycle-accurate memory-access logging of its own.
Paper and code
The framework is described in GainSight: A Unified Framework for Data Lifetime Profiling and Heterogeneous Memory Composition.
The work was funded under a US Department of Defense microelectronics program and the paper carries a public-release distribution statement. The published paper points at a source repository in my former research group’s namespace, which I no longer control; I am mirroring the code to my own account and this page will link there once that is done.