Multi-primitive in-memory computing for Monte Carlo tree search
Monte Carlo tree search (MCTS) enables artificial intelligence (AI) decision-making, but requires 55-300 W on conventional processors, limiting edge deployment. In-memory computing (IMC) is energy-efficient on regular workloads but has been considered incompatible with irregular multi-phase algorithms. We introduce phase-to-primitive decomposition, which reformulates each algorithmic phase as a hardware-native IMC primitive. Applied to MCTS, selection, expansion, rollout and backpropagation map to content-addressable memory, combinational logic, a resistive random-access memory (RRAM) crossbar and static random-access memory, keeping search on chip. At 22 nm with fabricated RRAM-array par...
| Authors | Tergel Molom-Ochir, Benjamin F. Morris, Yintao He et al. |
|---|---|
| Primary category | cs.AR |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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