Heterogeneous Compute for AI Workloads

Arm plays a central role in heterogeneous compute by offering a unified architecture that integrates CPUs, GPUs, NPUs, and interconnects. This allows developers to deploy the right compute engine for each task, optimizing performance and energy efficiency across AI, graphics, and control workloads.

Heterogeneous compute in AI refers to using multiple types of processors, such as CPUs, GPUs, and NPUs, within a single system to run different parts of an AI workload. Each processor type is optimized for specific tasks: CPUs manage general-purpose logic; GPUs handle high-throughput parallel operations; and NPUs accelerate machine learning inference. On Arm-based platforms, this architecture enables efficient, scalable AI execution from cloud to edge, improving performance, energy efficiency, and responsiveness for complex models like those used in generative AI.

Heterogeneous computing improves AI performance by distributing workloads across specialized processing units—CPUs, GPUs, and NPUs—each optimized for different tasks. On Arm-based platforms, this approach enables efficient use of compute resources: CPUs handle control and logic, GPUs accelerate parallel processing, and NPUs (where available) optimize AI inference. This division helps reduce bottlenecks, lower power consumption, and increase throughput, so AI models, especially generative ones, can run faster and more efficiently across cloud, edge, and endpoint environments.

Industries that rely on AI, real-time processing, or high-performance computing benefit most from heterogeneous computing. These include:

• Automotive: Enables advanced driver-assistance systems (ADAS) and in-vehicle AI by distributing tasks across CPUs, GPUs, and NPUs.
• Healthcare: Supports medical imaging and diagnostics through parallel processing and low-latency inference.
• Consumer technologies: Powers AI-driven experiences in smartphones, wearables, and smart home devices with efficient on-device compute.
• Cloud and datacenter: Improves throughput and energy efficiency for large-scale AI workloads and generative models.
• Industrial IoT: Enhances predictive maintenance and automation with efficient edge AI processing.

Heterogeneous computing allows each of these sectors to optimize for performance, power, and responsiveness based on workload needs.