Today we have a great variety of multimedia applications, including the ape plications for audio processing, speech recognition, automatic machine translation, text-to-speech, speaker recognition, and authentication. We also have image classification, object (face, person) detection, and semantic segmentation for imaging prospects.
These images require a lot of computes in order to enhance the image quality. This can be done with a wide array of products, such as mobile devices, automotives, wearables, and the Internet of Things (IoT). A lot of this can also be done using the edge devices but often we tend to offload a lot of the computing into cloud and data centres.
Spectrum of compute engines for multimedia
There are many options available at hand today. These are various compute engines (see Fig. 1) where you have varying degrees of energy efficiency and flexibility.
The left most side in Fig. 1 has the lowest energy efficiency but with higher flexibility. There are central processing units (CPUs), graphics processing units (GPUs), general neural processing units (NPUs), specialised NPUS that can take on various types of nn workloads and then you can also go for higher level of energy efficiency with specialised engines.
The right most side engine can process a significantly larger amount of data with the highest efficiency. It comes at a tradeoff for least amount of flexibility.
Specialised engines for high efficiency
The following are some of the examples for specialised engines.
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