Adlik is a toolkit for accelerating deep learning inference. The goal of Adlik is to accelerate deep learning inference process both on cloud and embedded environments. Adlik consists of two sub projects: model compiler and serving platform. Model compiler supports several optimizing technologies like pruning, quantization and structural compression to optimize models developed in major frameworks like Tensorflow, Keras, and Caffe, so that they can run with lower latency and higher computing efficiency. Serving platform provides deep learning models with optimized runtime based on the deployment environment such as CPU, GPU, and FPGA. Based on a deep learning model, the users of Adlik can optimize it with model compiler and then deploy it to a certain platform with serving platform.

With Adlik, different deep learning models can be deployed to different platforms with high performance and much flexibility.

Adlik is an incubation-stage project of the LF AI & Data Foundation.

Contributed by: ZTE in September 2019

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