Evolutionary Mapping of Neural Networks to Spatial Accelerators

• URL: http://arxiv.org/abs/2602.04717v1
• Date: Wed, 04 Feb 2026 16:28:08 GMT
• Title: Evolutionary Mapping of Neural Networks to Spatial Accelerators
• Authors: Alessandro Pierro, Jonathan Timcheck, Jason Yik, Marius Lindauer, Eyke Hüllermeier, Marcel Wever,
• Abstract summary: We introduce the first evolutionary, hardware-in-the-loop mapping framework for neuromorphic accelerators.<n>We evaluate our approach on Intel Loihi 2, a representative spatial accelerator featuring 152 cores in a 2D mesh.<n>Our method achieves up to 35% reduction in total latency compared to default cores on two sparse multi-layer perceptron networks.
• Score: 64.13809409887254
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatial accelerators, composed of arrays of compute-memory integrated units, offer an attractive platform for deploying inference workloads with low latency and low energy consumption. However, fully exploiting their architectural advantages typically requires careful, expert-driven mapping of computational graphs to distributed processing elements. In this work, we automate this process by framing the mapping challenge as a black-box optimization problem. We introduce the first evolutionary, hardware-in-the-loop mapping framework for neuromorphic accelerators, enabling users without deep hardware knowledge to deploy workloads more efficiently. We evaluate our approach on Intel Loihi 2, a representative spatial accelerator featuring 152 cores per chip in a 2D mesh. Our method achieves up to 35% reduction in total latency compared to default heuristics on two sparse multi-layer perceptron networks. Furthermore, we demonstrate the scalability of our approach to multi-chip systems and observe an up to 40% improvement in energy efficiency, without explicitly optimizing for it.

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