Delegation

Delegation operates at the granularity of individual neural network operators (e.g., Conv2D, MatMul, ReLU). The runtime's graph partitioner analyzes the model graph and identifies subgraphs of supported operators that can be executed entirely on the accelerator. Unsupported operators, or 'fallback' ops, are executed on the main CPU. This allows for heterogeneous compute, where a single model is split across multiple processors on a System-on-Chip (SoC).

• Graph Partitioning: The process of splitting a model into accelerator-compatible and CPU-only subgraphs.
• Fallback Mechanism: The CPU execution path for operators not supported by the accelerator, ensuring functional completeness.

A delegation is not a generic handoff. It requires a backend compiler that translates the standard model representation (e.g., TFLite, ONNX) into highly optimized, proprietary machine code for a specific accelerator. This compiler applies hardware-aware optimizations like operator fusion and memory tiling. For example, delegating a model to a Qualcomm Hexagon DSP uses the Qualcomm Neural Processing SDK, while delegating to an Apple Neural Engine uses Core ML's ANECompiler.

• Backend Compiler: A toolchain that converts a standard model format into accelerator-specific instructions.
• Proprietary Kernels: Hand-tuned, closed-source implementations of common neural network operations for a specific hardware target.

The delegation API defines the execution contract between the main CPU and the accelerator. In synchronous delegation, the CPU thread blocks until the accelerator completes the subgraph and returns the result. In asynchronous delegation, the CPU submits the workload and continues processing other tasks, using synchronization primitives (like fences or events) to wait for the result when needed. Asynchronous execution is crucial for overlapping data preprocessing on the CPU with model inference on the NPU, maximizing pipeline utilization in streaming medical data applications.

A major bottleneck in delegation is data transfer between CPU memory and accelerator memory. Advanced delegates implement zero-copy architectures using shared memory regions or hardware DMA engines. Instead of copying tensor data, the runtime passes a memory handle or pointer. For instance, an Android Neural Networks API (NNAPI) delegate can use Android Hardware Buffers (AHardwareBuffer) to share image data directly from a camera sensor to an NPU without a CPU-mediated copy, drastically reducing latency and power consumption for real-time medical imaging.

A robust delegation framework must handle runtime failures gracefully. If an accelerator encounters an error (e.g., an unsupported dynamic shape or a timeout), the runtime must catch the exception and transparently fall back to a CPU implementation of the failed operator. This ensures that a medical device does not crash during a critical inference. The framework may also implement a denylist of operators that are known to be buggy on a specific driver version, forcing them to run on the CPU from the start to guarantee deterministic, reliable execution.

Delegation is tightly coupled with model quantization. An NPU typically requires a specific quantization scheme, such as symmetric per-channel INT8 quantization. The delegation contract explicitly defines the supported data types and quantization parameters. The runtime must verify that the model's quantization matches the accelerator's requirements before offloading. If a model is quantized with a non-supported scheme (e.g., per-tensor quantization when the NPU requires per-channel), the delegation will fail, and the runtime must either reject the model or insert a dequantization/quantization (dequant/quant) conversion layer on the CPU.

A graph optimization strategy that combines multiple discrete neural network operations into a single computational kernel. This reduces memory access overhead and is critical when delegating operations to accelerators with limited on-chip memory.

• Merges convolution + batch normalization + activation into one kernel
• Eliminates intermediate tensor reads and writes to DRAM
• Reduces delegation overhead by minimizing kernel launch count
• Essential for meeting strict latency budgets on medical wearables

An execution model that distributes an AI workload across different types of processors on a System-on-Chip. Effective delegation requires intelligent scheduling across CPU, GPU, DSP, and NPU based on each operation's characteristics.

• CPU handles control flow and irregular operations
• GPU accelerates parallelizable general-purpose compute
• NPU executes quantized neural network primitives
• DSP processes sensor fusion and signal preprocessing
• The runtime scheduler must balance power vs. latency dynamically

A technique that partitions a deep neural network to execute initial layers on a local edge device and remaining layers on a nearby server. This is a form of partial delegation that balances latency and computational load.

• Early feature extraction runs on-device for privacy
• Compute-heavy later layers offloaded to an edge gateway
• Reduces end-to-end latency vs. full cloud offload
• Split point chosen based on network bandwidth and device capability
• Enables complex diagnostic models on resource-constrained wearables

The execution of a neural network using 8-bit integer arithmetic. This is the standard quantization scheme that enables efficient delegation to NPUs and DSPs, which are optimized for integer math.

• Reduces model size by 4x compared to FP32
• Integer operations execute 2-4x faster on accelerator hardware
• Requires a calibration dataset to determine optimal quantization ranges
• Minimal accuracy loss for most medical diagnostic models
• Enables continuous inference on battery-operated implantables