arXiv:2605.26118v1 Announce Type: cross Abstract: Porting deep learning algorithms to new hardware accelerators requires developers to repeatedly apply the same low-level optimizations -- quantization, memory access coalescing, tile size tuning, and architecture-specific workarounds -- to every Triton kernel in their code-base. This manual, repetitive effort is a major bottleneck: each kernel demands the same cycle of trial-and-error profiling against hardware constraints that vary across devices, yet the underlying optimization patterns remain largely consistent. We present Xe-Forge, a multi-
The proliferation of AI models and varied hardware accelerators, particularly from Intel, drives an urgent need for more efficient and automated kernel optimization techniques, reducing the bottleneck of manual porting.
Automating kernel optimization for GPUs, especially with LLMs, significantly reduces development time and expertise required, making AI deployment more accessible and efficient across diverse hardware.
The reliance on manual, repetitive low-level optimization for hardware-specific AI deployment is reduced, fostering faster iteration and broader hardware compatibility for deep learning applications.
- · Intel
- · AI developers
- · Deep learning deployment
- · GPU manufacturers
- · Manual optimization specialists
- · High-latency AI development workflows
Xe-Forge directly improves the efficiency and speed of porting AI algorithms to Intel GPUs.
This efficiency gain could accelerate the adoption of Intel GPUs in the deep learning ecosystem, challenging Nvidia's dominance.
Easier optimization through LLMs could lead to specialized AI models becoming viable on a wider array of commodity hardware, democratizing access to high-performance AI inference.
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Read at arXiv cs.AI