ARTIFICIAL INTELLIGENCE CHIPS A brief overview of AI chips vs traditional chips

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AI chips are being implemented in a growing number of end-user products as demand for enhanced computational power and on-device AI grows. But what’s the difference between them and the traditional chips that we’ve used for decades?

As AI continues to evolve, the gulf between traditional chips and AI chips grows bigger. Although the two are similar in some respects, they are fundamentally different in their architecture, purpose, and applications.

Furthermore, as more chip manufacturers look towards providing on-device AI capabilities in their upcoming product lines, there’s likely to be a growing departure from the traditional CPUs that have acted as the engine for technological progress for decades.

But what exactly are AI chips, and how do they differ from traditional CPUs?

What are AI chips?

AI chips are specifically designed to accelerate artificial intelligence tasks. These tasks include, but are not limited to, machine learning, neural network processing, and deep learning. AI chips are optimized for parallel processing, a requirement for the massive amounts of computations needed in the training and inference phases of AI models.

AI chips encompass all types of processing units, from GPUs and CPUs to TPUs and FPGAs, and as companies like Nvidia continue to make strides in developing high-end products, regulators are stepping in to try and control the technology, potentially falling into the wrong hands. In October, the U.S. Department of Commerce told Nvidia to halt shipping its AI chips to China. Despite this, some sales went ahead in January.

How do AI chips work?

AI chips function by optimizing the processing of AI and machine learning (ML) algorithms, focusing on efficiency and speed for tasks such as pattern recognition, natural language processing, and predictive analytics.

In AI chips, the fundamental building blocks of the chip are identical to those seen in traditional chips: They’re both silicon-based integrated circuits with transistors etched into them. Where they differ is how they process data.

AI chips primarily focus on the computational aspects, managing the heavy data processing demands of AI tasks—challenges that surpass what general-purpose chips such as CPUs can handle. To meet these demands, they often utilize numerous smaller, quicker, and more efficient transistors. This architecture enables them to execute more operations per energy unit, leading to higher processing speeds and reduced power usage compared to chips with fewer but larger transistors.

AI chips also feature capabilities that accelerate the computations required by algorithms, such as parallel processing, which enables them to perform multiple calculations at the same time.

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Differences between traditional and AI chips

Here’s a look at some of the ways traditional and AI chips differ.

Architecture
Traditional chips are designed to handle a broad range of tasks but excel in sequential processing. They operate well under a wide variety of workloads, including running operating systems, office applications, and web browsing.
In contrast, AI chips are designed to perform parallel processing, enabling them to handle multiple operations simultaneously. This is crucial for speeding up the computation-heavy tasks in AI and ML, such as matrix multiplications and deep learning algorithms.

Applications
Conventional chips are versatile, serving as the backbone of everyday computing needs in personal computers, servers, and mobile devices.
AI chips, however, are tailored for AI-specific applications, including voice recognition, language translation, image processing, and autonomous vehicles. Their specialized nature makes them ideal for data centers, AI research facilities, and embedded systems in consumer electronics that utilize AI functionalities.

Manufacturing costs
AI chips can be more expensive to design and manufacture than conventional chips due to their specialized hardware and the need for advanced technologies to achieve high levels of parallelism and computational power. However, the cost can be offset by the efficiency gains and the specific value they bring to AI applications.

Efficiency and performance
AI chips often offer greater efficiency and performance for AI tasks compared to conventional chips. This is because they reduce the need for data to be moved back and forth between the memory and the processor, a common bottleneck in conventional computing architectures.

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The future of AI chips

As we realise the raw power and promise of AI, demand for AI chips has rocketed. While this is creating various supply chain and geopolitical challenges that manufacturers will need to navigate, one thing is becoming apparent: AI chips may well become the go-to for most electronics applications in the future.

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