Custom TensorFlow Lite OP Pipeline: Architecture, Server and Client Implementation

This article presents an engineering‑focused guide to building a custom TensorFlow Lite (TFLite) OP pipeline. It is divided into four parts: overview, server‑side custom OP, client‑side custom OP, and extensions.

Overview introduces what a custom OP is and its four characteristics—speed, capability, security, and innovation. It explains the basics of TensorFlow Lite and the built‑in OPs.

Server‑side custom OP describes registration of a custom OP using the REGISTER_OP macro, implementation of the compute function, and compilation of the three‑tier libraries (server, Android, iOS). Build tools such as ndk‑build for Android and Makefile for iOS are discussed, together with command‑line options to reduce binary size.

Example registration code:

REGISTER_OP(ZeroOut, ...);

AddCustom(const char* name, TfLiteRegistration* registration);

Compilation steps are illustrated (e.g., invoking ndk‑build) and the need for a TensorFlow environment is noted.

Client‑side custom OP covers model and library download, MD5 verification, decryption (XOR‑based key), and in‑memory loading to avoid disk writes. The client registers the OP with the same registration structure used on the server:

void* (*init)(TfLiteContext* context, const char* buffer, size_t length);

void (*free)(TfLiteContext* context, void* buffer);

TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node);

TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node);

On iOS the AddCustom function can be called directly; on Android it must be exposed through JNI. Dynamic loading via dlopen is also described.

Extension discusses current limitations (large file size, limited data types) and proposes improvements such as model compression and support for additional tensor types (string, double64).

References to official TensorFlow documentation and related resources are listed at the end.