Hardware Mathematics for Artificial Intelligence

Why math and a library of mathematical IP cores is important for AI.


Article written by John A. Swanson, Sr. Product Marketing Manager, Synopsys

Artificial intelligence (AI) has the potential to fundamentally change the way we interact with our devices and live our lives. Petabytes of data efficiently travels between edge devices and data centers for processing and computing of AI tasks. The ability to process real world data and create mathematical representations of this data is a key component, and in some cases, a product differentiator. Accurate and optimized hardware implementations of functions offload many operations that the processing unit would have to execute. As the mathematical algorithms used in AI-based systems stabilize, the demand to implement them in hardware increases which is advantageous for many AI applications freeing compute resources with hardware implementations.

For AI, one of the important concepts to understand is a neural network. A neural network is defined as a computer system modeled on the human brain and nervous system. In hardware, this is a function that will “learn” an output for a given input. Training is “learning”. Showing the network many images of what you are training the network to recognize is implemented using math.

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