Heterogeneous compute is an execution strategy that distributes a single computational workload across multiple, architecturally distinct processor cores—such as a CPU, GPU, and NPU—on a single system-on-a-chip (SoC). The goal is to assign each subtask to the processor best suited for it, maximizing both performance and energy efficiency simultaneously.
Glossary
Heterogeneous Compute
An execution strategy that partitions a single AI workload across different types of processors, such as a CPU, GPU, and NPU, to optimize for both throughput and energy efficiency on a system-on-a-chip.
COMPUTE ARCHITECTURE
What is Heterogeneous Compute?
A processing paradigm that partitions a single AI workload across different processor types to optimize throughput and energy efficiency.
In edge diagnostic AI, this paradigm is critical. A CPU handles sequential control logic and DICOM network I/O, a GPU accelerates the massively parallel convolutions of a segmentation model, and an NPU executes quantized inference with minimal power draw. This orchestration enables real-time, scanner-side analysis within the strict thermal and power budgets of embedded medical devices.
ARCHITECTURAL PRINCIPLES
Key Features of Heterogeneous Compute Architectures
Heterogeneous compute partitions a single AI workload across multiple processor types—CPU, GPU, NPU, FPGA—to optimize throughput and energy efficiency on a system-on-a-chip (SoC). This approach is critical for deploying complex diagnostic models on power-constrained edge devices.
01
Workload Partitioning
The strategic decomposition of a neural network's computational graph to assign specific operations to the most efficient processor. Convolutions and matrix multiplications are offloaded to the GPU or NPU, while the CPU handles control logic, preprocessing, and sequential operations. This minimizes data movement bottlenecks and maximizes utilization of each accelerator on the SoC.
3-5x
Throughput Improvement
02
Memory Hierarchy Management
A critical design pattern for minimizing latency and energy consumption by keeping data as close to the compute unit as possible. Architectures leverage a tiered system:
- On-chip SRAM: For weights and activations in active computation
- LPDDR RAM: For larger model parameters and intermediate tensors
- Shared Unified Memory: To avoid costly data copies between CPU and GPU address spaces Efficient orchestration prevents the memory wall from stalling the inference pipeline.
~100x
Energy Cost of DRAM vs. SRAM Access
03
Concurrent Execution Scheduling
A runtime mechanism that overlaps computation across heterogeneous cores to hide latency. While the GPU processes one inference tile, the CPU can simultaneously fetch and preprocess the next tile from storage. This pipelining ensures that no single processor becomes a serial bottleneck, enabling sustained, high-throughput gigapixel inference for whole slide images.
< 5 ms
Scheduling Overhead Target
04
Power and Thermal Budgeting
Dynamic resource allocation based on real-time power and thermal constraints, essential for fanless, hermetically sealed medical devices. The system can throttle an NPU's clock frequency or migrate a workload from the GPU to a more efficient ASIC block to prevent overheating. This ensures consistent diagnostic performance without violating the device's thermal envelope or exceeding its power supply limits.
10-30W
Typical Edge SoC Power Budget
05
Hardware-Specific Kernel Optimization
The process of writing or compiling low-level compute kernels tailored to a specific processor's instruction set architecture. For example, a convolution operation might be implemented using NVIDIA CUDA cores on a GPU, Tensor Cores for mixed-precision matrix math, and a dedicated Deep Learning Accelerator (DLA) on an NVIDIA Jetson Orin. This fine-grained control unlocks the full performance potential of each heterogeneous unit.
INT8/FP16
Optimal Precision Formats
HETEROGENEOUS COMPUTE
Frequently Asked Questions
Clear, technically precise answers to the most common questions about partitioning diagnostic AI workloads across CPUs, GPUs, and NPUs on system-on-a-chip hardware.
Heterogeneous compute is an execution strategy that partitions a single AI workload across multiple types of processors—such as a CPU, GPU, and NPU—on a single system-on-a-chip (SoC) to optimize for both throughput and energy efficiency. Rather than running an entire diagnostic model on one accelerator, the workload is decomposed into distinct computational phases. A CPU might handle DICOM parsing and control logic, a GPU accelerates massively parallel convolutional layers, and an NPU executes recurrent or attention-based operations at ultra-low power. This orchestration is managed by a runtime scheduler that allocates each operation to the most suitable compute unit based on its arithmetic intensity, memory bandwidth requirements, and latency sensitivity. The result is a balanced pipeline that minimizes idle time and thermal throttling, which is critical for battery-powered, scanner-side diagnostic devices.
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About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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