Nova: An End-to-End MLIR Compiler for Deep Learning

• URL: http://arxiv.org/abs/2608.00029v1
• Date: Wed, 15 Jul 2026 05:16:28 GMT
• Title: Nova: An End-to-End MLIR Compiler for Deep Learning
• Abstract summary: We show how Nova can fuse operations across operation boundaries, optimize complex memory hierarchies, and tuning execution down to the register level.<n>By reducing the memory footprint by up to 29% relative to PyTorch, Nova successfully trains a 144-million parameter model at 17,900 tokens/s.<n> Nova matches or modestly exceeds cuBLAS and XLA on TF32 matmuls on most shapes, maintaining a stringent 5e-4 relative error.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The performance of deep learning models at scale relies heavily on how effectively high-level mathematical operations are mapped to underlying physical hardware. While high-level tensor frameworks provide flexible abstractions for model design, their eager execution models inherently lack the whole-graph visibility and granular control over hardware and memory required to maximize physical hardware utilization natively. To bridge this gap, we designed Nova, an automated end-to-end JIT compiler whose defining purpose is to achieve absolute control over this hardware mapping: fusing operations across operation boundaries, optimizing complex memory hierarchies, and tuning execution down to the register level. By capturing eager executions and unifying forward and backward passes into a single value-semantic dialect, Nova unlocks aggressive whole-graph optimizations. It then utilizes an Analytic Configurator to deterministically derive optimal execution schedules based on arithmetic intensity, dropping search time to zero. Backed by a structural hashing runtime, Nova synthesizes fine-grained kernels directly from the computation's structure. In our evaluations on an RTX 3060, Nova matches or modestly exceeds cuBLAS and XLA on TF32 matmuls on most shapes, maintaining a stringent < 5e-4 relative error. At the model level, Nova achieves up to 10.6% greater throughput than PyTorch and 4.4% greater than XLA on a 42-million parameter model, without compromising on numerical fidelity. Crucially, by reducing the memory footprint by up to 29% relative to PyTorch, Nova successfully trains a 144-million parameter model at 17,900 tokens/s where PyTorch encounters Out-Of-Memory (OOM) failures on the same 12 GB consumer GPU.

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