DeepStack introduces a fast performance model and hierarchical search method for co-optimizing 3D DRAM stacking, interconnects, and distributed scheduling in AI accelerators, delivering up to 9.5x throughput gains over baselines.
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MATCHA optimizes DNN deployment on heterogeneous multi-accelerator edge SoCs via constraint programming for memory and scheduling plus pattern matching for parallel execution, cutting latency up to 35% versus the MATCH compiler on MLPerf Tiny.
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DeepStack: Scalable and Accurate Design Space Exploration for Distributed 3D-Stacked AI Accelerators
DeepStack introduces a fast performance model and hierarchical search method for co-optimizing 3D DRAM stacking, interconnects, and distributed scheduling in AI accelerators, delivering up to 9.5x throughput gains over baselines.
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MATCHA: Efficient Deployment of Deep Neural Networks on Multi-Accelerator Heterogeneous Edge SoCs
MATCHA optimizes DNN deployment on heterogeneous multi-accelerator edge SoCs via constraint programming for memory and scheduling plus pattern matching for parallel execution, cutting latency up to 35% versus the MATCH compiler on MLPerf Tiny.