What is an AI accelerator?
AI accelerators (or AI accelerators) are hardware components that enable the acceleration of AI computing tasks. Accelerators are turbo processors that allow specific tasks such as pattern recognition, analysis of unstructured data, Monte Carlo simulations, streaming tasks or the construction of neural networks.
For AI tasks in general, conventional processors have not been sufficient for a long time and significantly faster graphics processors (GPUs) are used in many data centres. The computing operations of image processing are similar to those of Neural networks and therefore appropriate GPU use is worthwhile. However, such GPUs are not specifically designed for tasks of Deep Learning and therefore they quickly reach their limits.
The hardware thus forms a throughput bottleneck. In the meantime, however, many chip manufacturers are developing accelerators that can greatly increase the system's computing speed. AI accelerators are mainly available from the manufacturer Nvidia. Google, for example, uses the "Tesla P100" and the "Tesla K80" GPUs in its "Google Cloud Platform". High-performance system units are coming onto the market and there are "neuro-optimised" ASICs (Application-Specific Integrated Circuits). These are used in end devices such as smartphones, data glasses and IP cameras as well as in small devices. Such chips are only suitable for specific functions and are designed for this purpose. Special chips show their advantages in deep learning and highly accelerated supercomputers help with extensive AI calculations. Google's Tensor Processing Unit (TPU) in particular can boast its ASIC architecture for AI acceleration.
High performance computing (HPC) and hyperscale also bring more performance for AI calculations. Great hopes also lie in the Quantum computing - the computers of the future. Also promising for the future are neuromorphic microchips.
AI accelerator with add-on card or GPU?
Kontron now offers a new concept for use in artificial intelligence. The "Kontron Industrial AI Platform" offers high performance and with an add-on card it accelerates the calculations. Thus, the latest Smarc module will use the GPU to get more performance.
Artificial intelligence is gaining significant importance in the Intelligent Edge in industrial automation. The TPU (Tensor Processing Unit) supports small and low power applications with only 1 Watt for 2 TOPS. Thus, a simple USB camera without TPU offers only 6 frames per second and one with TPU offers five times the speed of 30 frames per second.
Industry 4.0 applications require a lot of computing power. Object recognition, classification and quality inspection of objects as well as predictive maintenance are used and are based on AI algorithms. Artificial intelligence is becoming increasingly important for point-of-sales applications. Advertising and relevant information should be placed in a more targeted manner. Add-on cards offer high performance and are ideal for special applications. GPUs, on the other hand, are inexpensive and generally useful for calculating AI tasks.
What AI accelerators are there?
The question is which hardware should be used that is as fast and efficient in operation as possible? There are two major application areas that play a role in AI. On the one hand, there is the particularly computationally intensive training of neural networks and, on the other hand, inferencing, i.e. drawing conclusions from incoming inputs, the actual AI performance.
Through training, the machine learning system learns from a variety of processed sample data. The quality of the inference of AI models can continue to improve over time. After the learning phase is completed, the AI system is even ready to assess unknown data. The framework TensorFlow for Deep Learning is used for the machine learning process. In the end, the AI application can classify production parts according to good parts and rejects.
Important AI accelerators are graphics cards from NVIDIA. GPUs specially optimised for AI can implement hundreds of parallel calculations and create a computing power of over 100 TeraFLOPS. AI users can choose between standard servers, GPUs and AI chips. Depending on the needs, appropriate hardware devices can be used. NVIDIA is really fast and only needs 75 watts in operation. Inferencing runs at a low power consumption. Recommended for training machine learning models is a Fujitsu NVIDIA GPU with Volta cores - such as a Tesla V100. Such cards are really big and occupy two slots. They consume a lot of power and have a higher price. For demanding requirements, there is the DLU for Deep Learning.
