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A neural net processor is a CPU that models how the human brain functions and places it on a single chip. One multi-cored chip may now do all the processing necessary for complicated applications like artificial intelligence (AI), machine learning, or computer vision instead of a vast network of computers.
Convoluted neural networks can currently span several computers and be implemented in a variety of ways using software. These contemporary solutions may make use of several CPUs, GPUs, or even FPGA devices that were specifically developed for speedier parallel processing.
Much to how computers shrank from being monstrously large machines, it is now conceivable to create processors that mirror the human brain’s one million neurons and 256 million synapses on a single chip.
The power needed for neural net computation can be decreased to one-tenth of what was previously needed thanks to this design. For improved efficiency in computer vision applications, other designs, including those from silicon chip design company Synopsys, incorporate embedded vision co-processors in addition to neural net processors.
The Global Neural networking processor market accounted for $XX Billion in 2021 and is anticipated to reach $XX Billion by 2030, registering a CAGR of XX% from 2022 to 2030.
With its “hundreds of watts”-powered Intel Nervana NNP AI chip, Intel is aiming for the GPGPU AI industry.
Since the beginning of AI inference, GPUs have pretty much controlled the industry. This is understandable given that GPUs are highly parallel processors that are perfect for inference.
However, Intel asserts that the issue with CPU AI performance has been resolved and demonstrated this claim with a competitive benchmark that showed them obtaining a 5x inference performance on a specially designed inference suite. It’s a very huge thing and might herald the start of an AI industry disruption if the GPU in issue was indeed a good one and priced reasonably.
The fact that the new AI chip would only require 100 watts of power is excellent news because power consumption and performance are inversely correlated. Their previous chip could only utilize milliwatts of power.
This indicates that they have successfully produced a performance improvement that will be hundreds of times greater than its forerunner. If the wrench benchmark displayed on screen, which has been accelerated by 5x, is even remotely accurate (on a generalized and repeatable basis), this will significantly alter Intel’s capacity to compete in the AI market.
