Unleashing the Potential of AI Inference Engines - What is an AI inference engine?

Before going to understand the AI inference engine, firstly you should get an idea about AI inference. Let's talk about AI Inference.

AI inference: AI inference refers to the process of using a trained machine learning model to make predictions or draw conclusions from new data.

AI inference : [{ Feed new data -> Pre-Trained model } - (conclusion) ]        

AI inference time: Inference time, also known as inference latency or prediction time, refers to the amount of time it takes for a trained machine learning model to process a new input and generate a prediction or output. It is the time required for the model to perform inference or prediction, which involves applying the learned knowledge to a new instance of data.

AI inference time : Prediction time        

An AI inference engine, also known as an inference system or reasoning engine, is a component of an AI system that uses rules and logical reasoning to make predictions, draw conclusions, or generate insights from data.

AI inference engine : (Optimize and Accerlate the Inference process)GPU        

AI inference engines are also an important component in driving assistance and autonomous vehicles, enabling lane departure detection and collision avoidance, and other capabilities

Types of AI inference engines

• Rule-based inference engines
• Bayesian inference engines
• Fuzzy logic inference engines
• Neural network inference engines
• Genetic algorithm inference engines:
• Decision tree inference engines

AI inference engines are being developed by

• Technology companies: Google, Microsoft, IBM, Amazon, Meta & more
• Startups: Cognitivescale, Ayasdi, and Numenta.
• Research institutions: universities and national labs
• Open-source communities: TensorFlow, PyTorch

Nvidia has developed several AI inference engines, which are software libraries that are optimized for running AI models on Nvidia GPUs.

1. TensorRT: TensorRT is an inference engine that is designed to optimize and deploy deep learning models for production environments. It can be used to accelerate a wide range of applications, including image and speech recognition, natural language processing, and recommendation systems.
2. Triton Inference Server: Triton Inference Server is an open-source inference engine that is designed to support the deployment of deep learning models in production environments. It can be used to serve multiple models simultaneously and supports a wide range of input and output formats.
3. DeepStream: DeepStream is an AI-based video analytics platform that includes an inference engine optimized for processing video data. It can be used to perform real-time object detection, classification, and tracking in video streams.

The first inference engine is often attributed to the expert system known as MYCIN, developed in the early 1970s by Edward Shortliffe and his team at Stanford University. MYCIN was designed to assist physicians in diagnosing and treating bacterial infections, and it used a rule-based system to make recommendations based on patient data and medical knowledge.

MYCIN was considered a groundbreaking achievement in the field of AI and paved the way for the development of more sophisticated expert systems and inference engines in the decades that followed.

Overall, AI inference engines are essential to modern AI systems and are likely to play an increasingly important role in shaping our technological future.

References :

https://developer.nvidia.com/ai-inference-software

https://www.xilinx.com/products/technology/ai-engine.html

https://www.arm.com/glossary/ai-inference

https://github.com/topics/inference-engine

https://www.alibabacloud.com/tech-news/deep-learning/gi6tclkgu4-deep-learning-inference-engine