PPL: A Full‑Platform Deep Learning Deployment Framework by SenseTime

Introduction – In the AI‑enabled era, SenseTime has developed the PPL inference framework, a high‑performance computing‑based deployment platform covering a toolchain, engine, and operator library to support smartphones, security, finance, and entertainment.

Core Components – PPL consists of a toolchain layer (quantization and model conversion), the PPL.NN inference engine supporting C++/Python and over 200 operators, and a multi‑backend high‑performance operator library for NN, CV, and domain‑specific tasks.

Platform Support – The framework runs on CPUs (ARM, x86, MIPS, RISC‑V), GPUs (Nvidia, mobile GPUs), DSPs (Cadence, CEVA, Qualcomm, TI) and DSAs (Huawei Ascend), providing optimized kernels for each architecture.

Key Features – Includes PPQ quantization (int4/int8/int16/fp16/fp32) with SOTA algorithms, PPL.CV image‑processing library, domain‑specific acceleration, and CUDA‑based optimizations such as implicit GEMM, auto‑tuning, and runtime compilation.

Performance – Benchmarks on mobile ARM, mobile GPU, Qualcomm DSP and Nvidia Tesla T4 show significant speed‑up and lower memory usage compared with competing solutions.

Future Outlook – Plans cover broader domestic chip support, large‑model training/inference, deep‑learning compilation, and AI for Science applications.

Q&A Highlights – PPL supports dynamic shapes, multiple backends, and aims to be framework‑agnostic while focusing on high performance across heterogeneous devices.