The World Model Planning Accelerator (WMPA): Hardware-Algorithm Co-Design for Embedded AI in Robotics
Overview
This paper introduces the World Model Planning Accelerator (WMPA), a chip architecture designed for embedded robotics to run world models efficiently. The authors analyze the energy and thermal bottlenecks of current GPU-based recurrent state space models, deriving theoretical lower bounds on computation and dynamic energy. They show that existing implementations are memory-bandwidth bound and generate excessive heat, reaching 556 K in simulations. To address this, WMPA integrates near-memory computation, variable-precision units, and copy-on-write scratchpads to target a 40x reduction in power consumption, keeping silicon tiles near ambient temperatures (306 K) in simulation, aiming to enable practical planning on thermally constrained robots.
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Reported quantity · energy consumption: 5.9 other · Basis not reported
“hardware sits from it, have not been characterized. We derive lower bounds on the arithmetic operations and the CMOS dynamic energy of a latent state transition from information theory and Boolean circuit complexity. Profiling a DreamerV3-style Recurrent State Space Model (RSSM) shows that every rollout operation is memory-bandwidth bound and that a GPU consumes at least $5.9\times10^{10}$ times the CMOS energy floor”
Exact source · revision 1Source owner not reported
Reported quantity · power reduction target: 40 other · Basis not reported
“entropy estimation, variable-precision rollout units, and copy-on-write latent scratchpads. With a design target of 40x lower power, the same tile stays near ambient at 306 K in simulation, pointing toward practical planning on thermally constrained robots.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalThe World Model Planning Accelerator (WMPA): Hardware-Algorithm Co-Design for Embedded AI in Robotics
This paper introduces the World Model Planning Accelerator (WMPA), a chip architecture designed for embedded robotics to run world models efficiently. The authors analyze the energy and thermal bottlenecks of current GPU-based recurrent state space models, deriving theoretical lower bounds on computation and dynamic energy. They show that existing implementations are memory-bandwidth bound and generate excessive heat, reaching 556 K in simulations. To address this, WMPA integrates near-memory computation, variable-precision units, and copy-on-write scratchpads to target a 40x reduction in power consumption, keeping silicon tiles near ambient temperatures (306 K) in simulation, aiming to enable practical planning on thermally constrained robots.
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