EdgeKernelBench: Unified LLM-Driven Kernel Generation for Edge Backends
Recent works show that LLMs can generate kernels with strong performance on server GPUs. Edge deployment presents distinct challenges because the hardware is heterogeneous, the software stacks are fragmented, and the resource budgets are tight. LLM kernel generation across diverse edge backends has yet to be studied systematically. We introduce EdgeKernelBench, which combines a
Reading in Plain English mode — toggle below to switch
Recent works show that LLMs can generate kernels with strong performance on server GPUs. Edge deployment presents distinct challenges because the hardware is heterogeneous, the software stacks are fragmented, and the resource budgets are tight. LLM kernel generation across diverse edge backends has yet to be studied systematically. We introduce EdgeKernelBench, which combines a
This work addresses the critical challenge of deploying LLMs efficiently on diverse and resource-constrained edge hardware. For ML engineers, understanding unified IRs and compiler interfaces for heterogeneous edge backends is crucial for optimizing model performance and reducing deployment friction, opening up new application areas for AI.