QNN LPAI Backend Glossary

LPAI

Low Power AI backend optimized for low-area, low-power applications such as always-on voice and camera use cases.

QNN_LPAI_API_VERSION_MAJOR

Major version of the QNN LPAI backend API. Current: .

QNN_LPAI_API_VERSION_MINOR

Minor version of the QNN LPAI backend API. Current: .

QNN_LPAI_API_VERSION_PATCH

Patch version of the QNN LPAI backend API. Current: .

QNN_SDK_ROOT

Environment variable pointing to the root directory of the QNN SDK installation.

LD_LIBRARY_PATH

Linux environment variable specifying paths to shared libraries required by QNN tools.

target_env

Specifies the target environment for model execution. Options: arm, adsp, x86.

enable_hw_ver

Specifies the hardware version of the LPAI backend. Options: v5, v5_1, v6.

fps

Frames per second setting for model execution. Default: 1.

ftrt_ratio

Frame-to-real-time ratio. Default: 10.

client_type

Type of workload. Options: real_time, non_real_time.

affinity

Core affinity policy. Options: soft, hard.

core_selection

Specifies the core number for execution. Default: 0.

profiling_level

Level of profiling detail. Options: basic, detailed.

is_persistent_binary

Indicates whether the context binary must persist until QnnContext_free is called.

QnnContext_createFromBinary

API used to create a QNN context from a serialized binary.

QnnGraph_finalize

API used to finalize a graph before execution.

QnnGraph_execute

API used to execute a finalized graph.

QnnContext_free

API used to release a QNN context.

QnnLpaiMem_MemType

Enum defining memory types: DDR, LLC, TCM, UNDEFINED.

QnnLpaiGraph_ClientPerfType

Enum defining client performance types: REAL_TIME, NON_REAL_TIME.

QnnLpaiGraph_CoreAffinityType

Enum defining core affinity types: SOFT, HARD, UNDEFINED.

Scratch Memory

Memory used for intermediate results that can be overwritten during execution.

Persistent Memory

Memory used for intermediate results that must persist across operations.

Backend Extension

JSON configuration enabling custom options for LPAI backend tools.

qnn-net-run

Tool used to execute QNN models on supported platforms.

qnn-context-binary-generator

Tool used to generate offline context binaries for QNN models.

qnn-profile-viewer

Tool used to visualize the profiling data.

QNN_CONVERTOR

Tool responsible for converting and quantizing models to QNN format.