Hardware-Aware Computing

What problem does it solve?

Writing software that exploits hardware topology to maximize performance on Apple Silicon, including unified memory and specialized units like Neural Engine and Metal GPU.

Core Features & Use Cases

• Understand heterogeneous SoC architecture (CPU, GPU, Neural Engine) and memory sharing via unified memory.
• Optimize data layout and compute paths (cache-aware design, SIMD pathways, and Metal compute) for real-time or large-scale workloads.
• Apply guidance across performance-sensitive tasks such as image processing, ML inference, and parallel compute on Apple Silicon, with recommended use of Accelerate framework and MTLBuffer shared memory.

Quick Start

Profile a workload on Apple Silicon and implement a cache-friendly, shared-memory path using Metal or Accelerate.