# Efficiency, Expressivity, and Extensibility in a Close-to-Metal NPU Programming Interface

arXiv: https://arxiv.org/abs/2504.18430 · HTML: https://mlsanity.com/paper/2504.18430/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2025.

## Built on by (most cited first, 9 of 9)

- [Dato: A Task-Based Programming Model for Dataflow Accelerators](https://mlsanity.com/paper/2509.06794/) (2025)
- [AIE4ML: An End-to-End Framework for Compiling Neural Networks for the Next Generation of AMD AI Engines](https://mlsanity.com/paper/2512.15946/) (2025)
- [From Loop Nests to Silicon: Mapping AI Workloads onto AMD NPUs with MLIR-AIR](https://mlsanity.com/paper/2510.14871/) (2025)
- [Mapping Gemma3 onto an Edge Dataflow Architecture](https://mlsanity.com/paper/2602.06063/) (2026)
- [From Human Guidance to Autonomy: Agent Skill System for End-to-End LLM Deployment on Spatial NPUs](https://mlsanity.com/paper/2606.07586/) (2026)
- [Taming the Exponential: A Fast Softmax Surrogate for Integer-Native Edge Inference](https://mlsanity.com/paper/2604.02292/) (2026)
- [TileFuse: A Fused Mixed-Precision Kernel Library for Efficient Quantized LLM Inference on AMD NPUs](https://mlsanity.com/paper/2606.11357/) (2026)
- [STEEL: Sparsity-Aware Fused Attention for Energy-Efficient Long-Sequence Inference on AMD's XDNA NPU](https://mlsanity.com/paper/2607.09385/) (2026)
- [Programming AMD XDNA NPUs with Open-source Compiler Tools: A FlashAttention Case Study](https://mlsanity.com/paper/2609.21264/) (2026)

## Abstract (arXiv)

Accelerators such as neural processing units (NPUs) deliver an enticing balance of performance and efficiency compared to general purpose compute architectures. However, effectively leveraging accelerator capabilities is not always simple: low-level programming toolkits may require substantial developer effort while high-level programming toolkits may abstract critical optimization features. This work aims to increase efficiency of designers using IRON, a toolkit for close-to-metal NPU performance engineers. We provide an updated programmer interface to IRON containing new and refined programming constructs. The new interface includes extensible features for placement and data transformation. These contributions are evaluated in terms of 1) efficiency, with analysis showing ~26% average reduction in lines of code and decreases in Halstead metrics for a variety of designs; 2) expressivity, demonstrating the new interface supports the wide range of features and patterns already supported by IRON; and 3) extensibility, illustrating the new tooling for placement and tiling can be extended to accommodate common use-cases.