Bao: Automatic Region Placement and Memory Allocation for Intermittent Computing

2026-10-01

AI Summary

This study addresses the suboptimal region partitioning and excessive energy consumption caused by local greedy strategies in intermittent computing. We propose a joint optimization method for energy-aware region formation and memory allocation. Built upon the LLVM compiler infrastructure, the problem is formulated as a mixed-integer linear program (MILP) that yields globally optimal solutions while providing formal correctness guarantees for state consistency and forward progress. Experimental results demonstrate that our approach improves execution speed by 10% and reduces region boundary hits by 52%, significantly lowering overall system energy overhead.

Abstract

Intermittent computing enables batteryless embedded devices to operate in harsh environment, but frequent power failures interrupt program execution and require careful management of state across power cycles. The core challenge is to guarantee both memory consistency and forward progress while minimizing the energy overhead of checkpointing. Recent compile-time approaches identify regions that fit in the energy buffer. At runtime, the device waits and recharges between the regions. However, they still rely on greedy or path-local heuristics that commit to local decisions and can miss globally lower-overhead boundary placements. To address this limitation, we present Bao, a system that jointly finds optimal energy-aware region formation and memory allocation decisions with formal correctness guarantees, by formulating it as a mixed-integer linear program. We prove that any feasible solution guarantees energy safety and implement our approach in LLVM. Our evaluation on 13 benchmarks across 3 capacitor sizes shows that Bao outperforms existing baselines, achieving 10% faster execution and 52% fewer region boundary hits on average compared to the best baseline.

Problem

• Intermittent Computing
• Region Placement
• Memory Allocation
• Checkpointing Overhead
• Energy-aware

Innovation

• Intermittent Computing
• Mixed-Integer Linear Programming
• Region Placement
• Memory Allocation
• Energy-aware Compilation