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Vision Chips: Implementing Vision Algorithms with Analog VLSI Circuits

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Manufacture benefits. Privacy Preferences I Agree. Their GPUs come with software drivers and computing toolkits that can run computing jobs in parallel. Machine learning algorithms are nothing but mathematical and statistical equations that are adapted to solve business problems.

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Training a machine learning model involves running complex mathematical equations in parallel by replacing multiple variables of an equation. When the magic combination of variables is discovered, the model is said to be fully trained. GPUs come with hundreds of cores that can parallelly run these complex mathematical equations.


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While CPUs are equally faster, they are general purpose processors that are not designed for massive parallelization. In supervised machine learning, training involves feeding a variety of combination of variables to complex equations until the prediction comes closer to the actual value. This technique is used for solving simple linear regression problems to complex deep learning algorithms dealing with computer vision and natural language processing. Once a model is fully trained, it is ready to predict or classify unseen data points.

Inferencing is a technique of utilizing a fully-trained model for prediction. Though inferencing is not as intense as training, it still uses complex mathematical equations for generating the expected outcome. For example, in computer vision, an input image is instantly converted into a massive array of pixels that is fed to a model for inference.

This kicks off a complex computational task that deals with millions of equations and variables. Most of the trained machine learning models run in offline mode at the edge computing layer. These edge devices need accelerators to speed up inferencing. If the inference is run only on a CPU, it may not result in a faster response.

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In scenarios such as face detection and object detection, users expect the prediction or classification to happen in milliseconds. The classic example of this use case is Face ID authentication in iPhone. When the camera captures a matching face, the phone is unlocked immediately. There is a GPU in iPhone that is accelerating the computation of a machine learning model.