We stand on the precipice of an Artificial Intelligence revolution, one that promises a world painted in broad strokes of optimism – but that revolution is precariously close to being stifled before it can take root by the burgeoning global data crisis.
Picture an AI-assisted doctor whose precise diagnoses could save up to 14 million lives per year. Visualize autonomous vehicles operating with such efficiency that they prevent 350,000 traffic fatalities annually. Imagine if artificial intelligence could predict natural disasters with an accuracy that feels like precognition. This world, a utopia guided by the invisible hand of technology, is what I, and many other professionals in our industry, envisioned when we pursued a career in science and technology.
Artificial Intelligence has the potential to revolutionize every facet of our lives, but our current data processing infrastructure, the backbone of our digital future, is woefully inadequate and, if we don’t act soon to update it, that halcyon future we dream of may remain a figment of our imagination.
Take a moment to remember the Robinhood app crash of 2021. That debacle, which resulted in a costly lawsuit, was the direct result of servers failing under the weight of data processing demands for millions of trades. The data required to execute trades at scale pales in comparison to the amount of data that generative AI and other next generation technologies will require daily.
To grasp the scale, consider a single search query on ChatGPT. It combs through a massive data set of 570GB – at more than ten times the cost of a standard search – and dwarfs the data required to execute a trade on the Forex market.
According to the International Data Corporation, the global data sphere is anticipated to burgeon to an astounding 175 zettabytes by 2025. This vast ocean of data is the lifeblood of AI. Without an infrastructure capable of quickly processing all that data, even the most sophisticated AI algorithms are rendered impotent, as useless as racing tires on a car with no engine.
So, how do we equip ourselves for this imminent AI gold rush? The key lies at the heart of our digital world: semiconductors.
The primary path forward involves leveraging the power of custom silicon. These tailor-made chips are purpose-built for AI processing tasks, ranging from the training of intricate neural networks to managing real-time inference tasks. By specifically catering to AI workloads and applications, they provide optimized performance and energy efficiency resulting in up to a 100x boost in performance and power reduction.
In addition to these custom solutions, the adoption of chiplets represent another promising direction for innovation. Like LEGO blocks, chiplets are pieces of semiconductors that can be easily put together to provide similar performance. By combining specialized chiplets for specific functions, AI systems can achieve higher performance and efficiency. We found that using chiplets can result in up to a 5x boost in performance and energy efficiency. This boost in performance enables faster and more accurate AI computations, enhancing the overall capabilities and effectiveness of AI applications. This trend is beginning to reverberate across the industry, as the global chiplets market is projected to advance at a CAGR of 40.9% from 2021 to 2031, and is estimated to reach US$ 47.19 Bn by 2031
The key to our transition into a technology-forward society lies not just in the programs our computers run, but in the infrastructure upon which they operate. We must realize that AI and data infrastructure are two sides of the same coin. One cannot exist without the other.
We have a choice. We can either embrace this challenge, fortify our data infrastructure, and stride boldly into a future where AI helps us reach unprecedented heights, or we can ignore the warning signs and find ourselves in a future mired with half-baked AI applications and unfulfilled potential. The AI gold rush is here. It’s time we start manufacturing better shovels.
About the Author
Tony Pialis is the co-founder and CEO of Alphawave Semi, a global leader in high-speed connectivity for the world’s technology infrastructure. A serial entrepreneur, Tony previously co-founded three semiconductor IP companies, including Snowbush Microelectronics (sold to Gennum/Semtech, currently part of Rambus), and V Semiconductor (acquired by Intel).
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