Heterogeneous Compute
What is Heterogeneous Compute?
Heterogeneous compute, also known as hybrid computing, refers to the use of different types of processors—like CPUs, GPUs, and AI accelerators—working together to handle diverse workloads efficiently. In Edge AI, this approach enables fast, energy-efficient processing directly on devices near the data source.
Why Is It Used?
It is used to accelerate AI workloads, reduce latency, and improve energy efficiency by assigning tasks to the most suitable processor type.
How Is It Used?
Devices leverage heterogeneous compute to process AI models locally on the edge, distributing tasks between CPUs for logic, GPUs for parallel computation, and AI accelerators for deep learning inference.
Types of Heterogeneous Compute
CPU + GPU: General processing with parallel computation for AI models.
CPU + AI Accelerator: Specialized AI computations for low-latency inference.
Full Hybrid: Combines CPU, GPU, and dedicated AI accelerators for maximum performance.
Benefits of Heterogeneous Compute
Low Latency: Real-time AI decision-making on edge devices.
Energy Efficiency: Optimized processor allocation reduces power consumption.
Scalability: Supports diverse workloads and evolving AI applications.
Cost Efficiency: Reduces reliance on cloud processing for AI tasks.