Retrospectively Reverse-Engineering Apple's Neural Engine

Apple’s Neural Engine was built for highly opinionated, CNN-era dataflows, and the reverse-engineering work reinforces that specialized NPUs can be less broadly useful than their headline specs suggest. For CIOs, the strategic takeaway is that AI hardware value comes from how well it matches real workload patterns—especially transformer/LLM inference—not from peak MAC counts alone, and Apple’s move to fold ANE capability into the GPU signals a broader shift toward more flexible acceleration platforms. IT organizations should treat purpose-built silicon as a workload-specific optimization, not a universal AI strategy, and plan for vendor roadmaps that may de-emphasize standalone NPUs in favor of GPU-centric designs.