learnedprocessors

A learned processor is a type of hardware accelerator designed to execute specific computational tasks that have been trained using machine learning techniques. Unlike traditional fixed-function processors or even programmable processors, learned processors are optimized for the patterns and structures learned by a machine learning model. This optimization can lead to significant gains in performance and power efficiency for the tasks they are designed for.

The development of learned processors often involves a co-design process between the hardware architecture and the machine learning model. The model's structure, such as its neural network layers and operations, informs the design of the processor's circuits. Conversely, hardware constraints can influence the types of models that can be effectively deployed. These processors are particularly relevant in applications like artificial intelligence inference, where rapid and energy-efficient execution of trained models is crucial. Examples include image recognition, natural language processing, and recommendation systems. They represent a shift towards hardware tailored to the demands of modern AI workloads.