AI Accelerator

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What is an AI Accelerator?

An Artificial Intelligence Accelerator or AI Accelerator is a system or chip that facilitates hardware acceleration for AI applications. They are typically utilized in processor-intensive tasks such as machine learning, artificial neural networks, machine vision, natural language processing, along with other AI workloads.

Data-intensive jobs like robotics and sensor-intensive ones like autonomous vehicle technologies typically require parallel processing, which is provided by the manycore architecture of AI accelerators.

History and Early Examples of AI Accelerator Usage

Hardware acceleration is not a brand new field. From as ancient as the 1990therefore, there were working versions of coprocessors that complemented the CPU’s ability to perform specific computational activities. Some of these task-specific accelerators include video graphics cards, GPUs, audio cards and digital signal processors (DSP).

Such hardware has been frequently employed for compute-intensive tasks like optical character recognition (OCR), audio signal processing and video processing. With the development of neural networks and other compute-heavy tasks, AI accelerators emerged as a new category of acceleration hardware.

Some of the recent cases of AI accelerators include Google’s Tensor Processing Unit (TPU) used in its TensorFlow program library and Mobileye’s EyeQ, which has been previously utilized in Tesla electric automobiles (EyeQ3 chip) because of its Autopilot function. The technology was so precious that Intel decided that it was worth acquiring the firm for 30 times its estimated yearly earnings for 2017 to get a total of $15 billion.

New Kids on the AI Accelerator Block

There is now no dominant technology in this area the manner Intel’s x86 CPUs dominated the world of personal computing. Various neurosynaptic architectures like ASICs (application-specific integrated circuits), FPGAs (field programmable gate arrays) and NNPUs (neural network processing units) co-exist, and also among the primary reasons is that their functions are super-specific to border computing applications like computer vision.

New AI accelerator hardware architectures are emerging on a regular basis. AI visionaries like NVIDIA’s CEO Jensen Huang foretell of a Cambrian-explosion-like phenomenon akin to its namesake phenomenon which occurred 500 million decades back when multi-cellular organisms began evolving rapidly into a myriad of forms encompassing every niche of ecology.

AI accelerators are anticipated to evolve in a similar manner since they can cater to each tier of cloud storage, high-performance computing, distributed cloud-to-edge, along with hyperconverged server. The focus is on speed, efficiency, and precision of processing AI workloads. To this conclusion, key sellers are hybridizing their product lines to include multiple technologies to achieve superior outcomes. One great instance of this is NVIDIA’s Volta GPU architecture or Intel’s Skylake CPUs being used alongside Google’s Cloud TPU.

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