How are Cognitive Computers Helping AI to Consume Less Energy

We have all heard how AI is making our lives more efficient, but the real question is, what makes AI efficient? The very soul of Artificial Intelligence is data, and globally, data centers consume 200 Terawatt-hours of power annually. This energy is enough to support 20 million households in the United States annually and can empower 333 million cars for a year.

A data center is nothing but a warehouse filled with computers computing large data rivers. Recently, the growing number of data centers across the globe has raised a consequentialist concern: emission. So, let’s discuss: What is the power problem Artificial Intelligence is facing? What are the solutions? Which company has come up with the solution?

The Power Problem

A wise man once said, “The best way to solve a problem is structuring it.” The quote finely resonates with the AI energy consumption problem we face today. Here, the problem is not energy generation for AI, but the overall energy consumption of AI-data centers. One can use this energy for various purposes, like making the environment sustainable and quality of life.

Circulating around how AI works, one thing grabs our attention: Data Centres. As the demand for artificial intelligence increases across industries, the demand for data centers grows, too. Data centers may be the most essential component of AI/ML; they help to compute, analyze, and store data for AI models. A very scalable data center can be as big as two average football fields, which is massive as it holds around 4200 of 24U racks.

This data center consists of 1000s of HPCs (High Performing Computers) that could help AI with various tasks that consume high energy power. Also, according to IEA, they are responsible for emitting 3% of total GreenHouse Gases globally. This is just one phase of the problem; the crucial one is providing energy consumption.

During the 100% energy utilization, a single data center facility consumes around 20 Megawatt. Below is the given rising power consumption chart that clears our speculations.

Global Electricity Demand of Data Centers 2010-2030

The lines here depict three possible scenarios: Best, Worst, and Expected. The worst scenario symbolizes data centers would not strategize their consumption of resources. It will lead to more emissions, global issues, and a power generation shortage. The expected scenario is that they reduce their consumption or strategize for efficiency. The third one, well, that is what we are going to discuss further: An efficient solution.

An Efficient Solution

When you break down the problem, you will get that there are two parts correlated to each other: The consumption part and the emission part. A company integrating technology and expertise for efficiency and scalability has introduced a microchip. This microchip is what will be the key to making AI energy efficient. The microchip makes AI the phoenix of the technological world.

A cognitive microchip that will help AI to consume less power. The ultimate aim of Artificial Intelligence is to think like humans, and cognitive computers can do that. They are like intelligent children, but they can think faster, learn faster, and process information using various means. The microchip can learn predictions and patterns and make decisions like humans. The three technologies cognitive computers utilize are Natural Language processors, machine learning, and AI. I know, right? We circle back to where we started. AI is helping itself to become more energy efficient, achieving Green- Tech, and reaching the sustainability goal every industry craves.

“The human brain can achieve remarkable performance while consuming little power.”  –Thanos Vasipoulos, Predoctoral Researcher, IBM

What Makes Cognitive Computers Energy Efficient

Who would have thought replicating human-like brain processing power could give us the following benefits? But here we are. These manycore microchips/cognitive computers can multitask at a very high-speed spectrum, allowing data centers to consume less energy. Another one is adaptive algorithms, where a data center can learn the most efficient way to perform a task. Lastly, these processors are designed to consume less energy and have an in-built energy-saving feature like smartphones.

The attributes that make the cognitive computers faster, better, and smarter are:

• Parallel processing
• Adaptive algorithms
• Low-power processing
• Energy saving features

Apart from this, cognitive computers also have numerous other benefits:

• Accurate Data analysis
• Improving customer interactions
• Enhance productivity and service quality
• Upgraded troubleshooting and anomaly detection

There are many benefits of cognitive computers yet to be explored; maybe exploring them will help us to reach the perfect solution. Sure, cognitive computers can eliminate numerous challenges data center faces, but some challenges could also hinder the implementation of these microchips.

What Will It Take to Implement the Algorithm?

Cognitive computers are powerful microprocessors that could lead data centers to a more efficient working phase. The chip can help data centers worldwide achieve the expected energy consumption trajectory. However, these high-speed data processing computers come with their own challenges. Here are a few of the challenges that could hinder the implementation

Complexity

The microchip we are discussing here can leverage artificial intelligence and machine learning. It hardwires their programs and algorithms to an IC. A tip of AI and IC can bring a lot of complexities. It becomes more complex when we are using it to drive data centers where multiple ICs are working together for an optimum result.

Although it’s an age of digital technology becoming intricate, it is essential to develop simplifying frameworks and platforms. So, every step we take towards making AI more efficient must also grow a branch of simplification. At last, if the technology cannot be used for the betterment of society, then it is not worth exploring.

Cost

The cognitive computers that would achieve the desired energy efficiency for data centers and AI systems are costly. Hopefully, government initiatives and cost-effective tech could contribute to it.

Skills Shortage

The primary issue, on the one hand, is that people are pretty optimistic about adapting AI; on the other, there are few doubts. Consequently, it creates a skill gap, and there are not many workers who can learn, adapt, and contribute to the development. Cognitive computing, AI, and big data tech are new-generation concepts that must be explored on a large scale. There needs to be programs, certifications, and educational institutes dedicated to it globally.

Conclusion

The new world powered by Artificial intelligence has pros and cons, and every solution does. It should be noted that we will preserve the benefits while the negatives can be eradicated. Sure, data centers used by AI have a massive energy consumption problem, but we are moving towards implementing more Green AI that consumes less energy.

A cognitive computer is an example, a milestone in this long journey of perseverance. In the end, achieving sustainability is the primary goal of every technological solution provider. A core ethos supporting society’s development should be adapted, integrated, and utilized for the most favorable outcome.

Originally published on ReadWrite by Chirag Leuva.