What’s This About: One of the next big moves for AI is the advancement of AI edge chips. These state-of-the-art chips, designed by leaders like Nvidia and IBM, will lessen both consumers’ and enterprises’ reliance on the cloud. As of right now, many technologies like Siri and other voice assistants can only operate with a cloud connection. But with AI chips, your smartphone, watch, or tablet will be able to make language translations, recommend songs, and complete many other complex tasks without a wireless or wifi connection.

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As the use of artificial intelligence (AI) technologies becomes more apparent throughout the world, taking over increasingly more complex tasks, AI chipsets will take over large-scale data processing. They will perform all the necessary computations and store their models on the device, such as a phone or tablet. AI chips will be the key player in transitioning AI technologies from the cloud to the actual device, and they can create an entire new realm of Internet of Things (IoT) devices. Consumer devices such as smartphones, tablets, wearables, and smart speakers rely on AI chips, along with enterprise market devices like robots, sensors, and cameras.

What Exactly is an AI chip?

AI chips, also referred to as AI hardware or AI accelerators, are specialized accelerators for AI-based applications, similar to graphics accelerator chips found in gaming consoles and high-end special FX workstations. More specifically, they are designed for the artificial neural network (ANN), which is a machine learning approach that mirrors the human brain in many ways. ANNs consist of layers of artificial neurons, and they can form deep networks with multiple layers. These networks are used for deep learning, which takes place when an ANN is fed massive amounts of data, which it then identifies patterns within. The ANN then uses this insight to make predictions based on new inputs.

While general purpose chips are capable of running these types of applications, AI chips are much more efficient. AI chips are customizable, which is extremely important for these applications, and they consist of three main parts: computing, storage, and networking.

Huge Revenue Driver

AI chips promise to revolutionize industries in many different ways. Here are a few key statistics demonstrating how they will be a huge revenue driver:

The consumer AI chip market is much larger than the enterprise, but it is only expected to grow at a compound annual growth rate (CAGR) of 18% between 2020 and 2024, compared to the enterprise AI chip market at 50%.

Deloitte Insights predicts the sales of AI chips will exceed 1.5 billion by 2024, putting annual unit sales growth at 20% or higher, which is double the longer-term forecast of the semiconductor industry.

Cloud to Edge

One of the biggest implications of the shift to these AI chips that operate on devices, or on the “edge,” is that there will be less and less reliance on the Cloud. Because AI computations require an extreme amount of intensive processing, they have been almost entirely performed remotely in data centers, on telecom edge processors, or enterprise core appliances.

Newly developed edge AI chips have completely shifted this dynamic, allowing these same computations on local devices. By running AI processing on the edge, it can take place on devices not connected to any cloud network.

Edge AI chips enable this process due to their small physical build, inexpensiveness, requiring less power, and generation of much less heat. All of this means they can be integrated into both consumer handheld devices like smartphones and industrial devices like robots.

There are two notable benefits to this approach:

Speed: Processing can be drastically sped up since it takes place locally rather than remotely.

Privacy: Because processing takes place locally rather than the Cloud, there are far less security and privacy concerns.

Many of the applications dependent on AI computations, such as biometrics and voice recognition, occur on smartphones, which will see the biggest implementation of AI chips in the near future. Smartphones currently account for over 70% of all consumer AI chips on the market.

Siri is one of the most popular AI technologies available to consumers, but it’s major downfall is that it’s not available when the device is offline. This is due to the fact that Siri relies on a network to operate, which would not be the case with a similar technology that operates on the edge.

State-of-the-art AI Chips

The first commercially available AI chip is still a recent development. In 2017, Intel launched the “world’s first self-contained AI accelerator in a USB format,” which enabled host devices to process deep neural networks natively, creating a cost-efficient way to run offline AI applications.

AI chips have come a long way in a short time. Here is a look at some of the leaders in the market (and potential great investment opportunities):

Nvidia: In 2020, Nvidia released its Nvidia A100 artificial intelligence chip, which is able to undertake supercomputing tasks.

ARM: Two of the recent chip designs have been released by ARM. The Arm Cortex-M55 and Ehos-U55 can be paired together for more complex uses.

Intel: Intel became a major leader in the industry after it acquired startup Habana Labs for $2 billion in 2019.

IBM: In 2021, IBM introduced the “world’s first energy efficient AI chip at the vanguard of low precision training and inference.”

Startups: Massive enterprises are not the only organizations working on AI chip technology. Startup companies like the California-based Blaize, which has overcome major AI processing limitations, and the Pittsburgh-based Tercero Technologies, which focuses on edge AI research, development, and integration, play key roles in the industry.

Challenges With AI Chip Technology

There are various challenges that companies face when developing AI chip technology:

Design: The technology requires incredibly large systems-on-chip (SoCs), and these rely on deep learning and hardware accelerators. This is especially challenging in the autonomous vehicle sector, where there are strict safety and reliability requirements.
Bandwidth: Neural network models are scaling up at such a fast rate that AI chips have trouble keeping pace. Deep learning models like OpenAI’s GPT-3 have 175 billion parameters.

Sparse representations: Neural networks are moving more toward sparse representations than dense. In a sparse array, the values are mostly zero, while in a dense array, they are mostly non-zero. Because of this, experts are trying to find ways to operate over sparse data structures, and if that can be done, there will be many new approaches developed.

Revolutionizing Markets With AI Chips

AI chips promise to revolutionize many sectors as the technology advances. Society will see this happen through consumer devices like smartphones and voice assistants, but there will be even more dramatic changes behind the scenes as the technology becomes more accessible to enterprises.

The increased use of AI chips will also take us into a world full of new IoT devices that we never encountered before, and while devices like these are often overshadowed by concerns regarding privacy and data use, edge AI chips are a step in the right direction as computations are performed offline.

What’s Next?

AI chips will be responsible for the next AI explosion, as specialized chips and the way they are designed are the future of the technology. Most people only hear about algorithms regarding AI technology, but hardware design is playing a crucial role in helping bring on the next breakthroughs.

Your Siri Might Soon Not Need the Cloud Anymore