AI Chips : A Step Forward to Transforming Computing World

"Artificial intelligence is not about building minds, it’s about the improvement of tools to solve them."

We can keep all the arguments and discussions aside and we can believe that today we are surrounded by devices. From smartphones to trimmer to door locks, we are surrounded and artificial intelligence is ingested in almost every device and it is remarkable, indeed. Our workload has been reduced in many aspects and we can thank the scientists and inventors. 

Yes! The guess is close to correct and the topic for today is Artificial Intelligence chips (AI chips). AI chips are specifically designed silicon chips to accelerate artificial intelligence applications like robotics, internet of things, data-intensive or sensor-driven tasks. Computer systems are often used with coprocessors, chips with specific-designated tasks like graphics card, sound card, graphics processing unit, and digital signal processors. As deep learning and artificial intelligence are rising, the concept of coprocessors is being implemented thus giving us AI chips.

In the 1990s, parallel high throughput systems were tried to be created for neural network simulations. FPGA-based accelerators were also first explored in the 1990s for both inference and training. ANNA was a neural net CMOS accelerator developed by Yann LeCun. In the 2000s, CPUs also gained increasingly wide SIMD units, driven by video and gaming workloads; as well as support for packed low precision data types. 

Deep learning frameworks are still evolving, making it hard to design custom hardware. Reconfigurable devices such as field-programmable gate arrays (FPGA) make it easier to evolve hardware, frameworks, and software alongside each other. While GPUs and FPGAs perform far better than CPUs for AI-related tasks, a factor of up to 10 inefficiencies may be gained with a more specific design, via an application-specific integrated circuit (ASIC). These accelerators employ strategies such as optimized memory use and the use of lower precision arithmetic to accelerate calculation and increase the throughput of computation.

 

In June 2017, IBM researchers announced an architecture intending to generalize the approach to heterogeneous computing and massively parallel systems. In October 2018, IBM researchers announced an architecture based on in-memory processing and modeled on the human brain's synaptic network to accelerate deep neural networks. The system is based on phase-change memory arrays. In February 2019, IBM Research launched an AI Hardware Center and claimed to have improved AI computing efficiency by 2.5 times every year intending to improve efficiency by 1000 times within a decade. 

 

IBM reported two key developments in their AI efficiency quest. First, IBM will now be collaborating with Red Hat to make IBM’s AI digital core compatible with the Red Hat OpenShift ecosystem. This collaboration will allow for IBM’s hardware to be developed in parallel with the software so that as soon as the hardware is ready, all of the software capability will already be in place. Second, IBM and the design automation firm Synopsys are open-sourcing an analog hardware acceleration kit — highlighting the capabilities analog AI hardware can provide.

 

The artificial intelligence chip market was valued at $6,638 million in 2018 and is projected to reach $91,185 million by 2025, registering a CAGR of 45.2% from 2019 to 2025. AI helps to eliminate or minimize the risk to human life in many industry verticals. The need for more efficient systems to solve mathematical and computational problems is becoming crucial owing to the increase in the volume of the data. 

Thus, the majority of the key players in the IT industry have focused on developing AI chips and applications. Furthermore, the emergence of quantum computing and the increase in the implementation of AI chips in robotics drive the growth of the global artificial intelligence chip market. In addition, the emergence of autonomous robotics—robots that develop and control themselves autonomously—is anticipated to provide potential growth opportunities for the market.

 

In terms of the benefits of AI chips, security and privacy are least compromised. AI chips which are applicable for deep neural networks have the lowest latency. This means that the chances of them getting concealed are the lowest. The networks are hinted at in their application. Another advantage of AI Chips is the fact that it has a much lower power consumption. Normal general-purpose chips were really inefficient. But AI chips enhance the speed of the AI processor to a greater extent.

 

Significant Factors impacting AI Chip Industry

 

Increase in demand for smart homes and smart cities

 

AI has the ability to provide impetus to initiate smart city programs in developing countries, such as India. Tools and technologies that are artificially intelligent possess a massive potential to transform interconnected digital homes and smart cities. Furthermore, the creation of a chip that embeds an inbuilt AI network has emerged as an opportunity for the artificial intelligence chip market.

 

Rise in investments in AI startups

 

Multiple countries, especially the U.S., witness considerable growth in tech start-ups every year, which are backed by various venture capitalists and venture capitals, thus increasing the market scope. Various key players have been innovating to build a dedicated platform.

 

Emergence of quantum computing

 

Quantum computers take seconds to complete a calculation that would otherwise take thousands of years. Quantum computers are an innovative transformation of artificial intelligence, big data, and machine learning. Thus, the emergence of quantum computing fuels the growth of the artificial intelligence chip market.

 

Apart from these, the dearth of skilled force and adoption of AI in developing regions play key roles in making the AI chip industry a boom. Furthermore, the development of smarter virtual assistants is opportunistic for the overall market. A notable illustration is Jarvis Corp, which is a start-up in the conceptual phases, to build a virtual assistant that answers questions by accessing the internet and acting as an internet server and as a control for connected devices.

 

AI and Machine Learning are developing fastly and getting adjusted to our daily life devices and AI chips are like the heart of these devices; faster, compatible, and efficient. With such a larger domain comes larger challenges and responsibilities and the brainy people are doing pretty well in maintaining them.

 

So, here is my question, easy and simple:

 

What might have been the motivation behind the first silicon chip? 

 

Answer it in the comment box and get a shoutout from the IEEE team. Do comment and share your views and suggestions.