The Insider's Guide to Artificial Intelligence Chips

The most upgrading, unstoppable field during today’s technological era is artificial intelligence. Approximate maximum IT industries all over the world trying to invest into this field. But the heart of this field is data more than the learning of data, which results in the efficiency of a good model. To overcome this storage, portability and efficiency of models the AI chips were  introduced. AI chips is the microprocessor which uses the artificial intelligence model to process with high speed and low consumption of power as well as storage.

WHAT IS AI ENABLED CHIPS?

AI chips were discovered by LeCun and Hinton. The deep neural network which would work as the human brain requires more computation units to perform actions as well as more data processing speed. AI chips have a unique memory access profile to ensure maximum bandwidth. The data flow on the chip is interconnected and must be optimized to ensure wide bandwidth paths when required to meet performance objectives, but allocating narrow paths when possible to optimize area, cost and power consumption. Each connection must also be optimized with a high level AI algorithm in mind to make it even more interesting.

WHY AI ENABLED CHIPS WERE INTRODUCED?

The term “artificial intelligence” was introduced by scientist McCarthy, Claude Shannon and Marvin Minsky  in 1956.  At the end of the decade “Arthur samuel” named the term ‘machine learning’ which means the machine can understand, analyze the data which would be provided by the user and according to provided data the machine would be able to generate output. In machine learning many models such as the McCulloch-Pitts model, Perceptron uses binary-activation-function. In this model the machine learning is done at a very low level.

To overcome this, in 1986 ‘Geoffery Hinton’ described the backpropagation algorithm which includes multiple layers of neurons between the input and output layer. In the past years, training of the model required two to three days. So to put down this thing the concept of custom-system-on-chips (CSOC) was introduced, which meant AI chips were the hardware device used to increase not only the processing speed but the efficiency of algorithms.

HOW AI ENABLED CHIPS IS BOON TO OUR REGULAR LIFE?

In today’s world almost all electronics and IT companies are willing to invest in artificial intelligence based projects. As we can take real life examples such as in joystick, automated cars, facial recognition systems, natural language processing (Alexa) and in many more devices AI chips are situated to perform the backend processes. Let’s take the example of a self-driving car. The main algorithm which is required to process this model is an image recognition system. In this model, rough images of each and every phase of the challenge is set in the database and according to that, the model is trained to give output to the car. According to the given training automobile parts of the car are working on the system calls.

Many more benefits such as:

Low connectivity:

 Which means the AI chips require low or no connectivity with the device.

Low latency requirement:

Performing AI computations at remote data centers requires low latency. Using edge AI chips reduces the latency to nanoseconds and the devices can easily function 

Power consumption:

The low power AI chips can be used in devices with small batteries to perform AI computations.

Processing huge data :

IoT devices produce a huge amount of data. Sending such huge data into the cloud requires immense cost and complexity but implementing machine learning processors on the endpoints makes the work easy. Using AI chips, the devices can easily analyze data and transfer only the useful data to the cloud thus demanding less cost and complexity.

FUTURE OF ENABLED AI CHIPS

With increase of interest in the field, old deep learning algorithms are becoming like rotten bananas, many of the programmers don’t like to work on the previous simple machine learning models, they are more interested to construct new deep neural network models, which would approximately think like a human brain, process like a human brain. Due to which the AI chips have to upgrade with new features, processing speed to  become clever in the future and more clever into the upcoming future. These chips will continue to leverage advances in semiconductor processing technology, computer architecture, and SoC design to boost processing power in order to enable the next-generation AI algorithms. At the same time, new AI chips will continue to advance in memory systems and on-chip interconnect architectures in order to feed new proprietary hardware accelerators with a constant stream of data required for deep learning.  

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