Beyond Silicon: Nurturing AI SoCs with IP

SoC designers face a variety of challenges when trying to balance specific computational requirements with the implementation of deep learning capabilities.

While artificial intelligence (AI) is not a new technology, it wasn’t until 2015 that a surge in new investment enabled advances in processor technology and AI algorithms. Aside from being considered just an academic discipline, the world began to take notice of this scientifically proven technology that could surpass human capabilities. Driving this new generation of investments is the evolution from AI in mainframes to embedded applications at the edge, leading to a significant shift in hardware requirements for storage, processing and connectivity in AI systems on chips (SoCs).

Over the past decade, AI has evolved to enable safer automated transportation, design household assistants tailored to individual user specifications, and create more interactive entertainment. In order to provide these useful functions, applications increasingly rely on deep learning neural networks. Compute-intensive methodologies and all-encompassing chip designs support deep learning and machine learning to meet the demand for Smart Everything. On-chip silicon technology must be able to provide advanced mathematical functions that enable unprecedented real-time applications such as face recognition, object and speech recognition, and more.

Define AI

There are three basic building blocks that most AI applications follow: perception, decision making, and response. With these three building blocks, the AI ​​is able to recognize its environment, use information from the environment to make a decision, and then naturally react to it. The technology can be divided into two broad categories: “weak AI or narrow AI” and “strong AI or artificial general intelligence”. Weak AI is the ability to solve specific tasks, while strong AI encompasses the machine’s ability to solve a problem when faced with a never-before-seen task. Weak AI accounts for most of the current market, while strong AI is seen as a forward-looking goal that the industry hopes to implement in the next few years. While both categories will bring exciting innovations to the AI ​​SoC industry, strong AI opens up a wealth of new applications.

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Download the infographic now: Views from leading manufacturers on Edge Computing and 5G

Machine vision applications are a driving catalyst for new investments in AI in the semiconductor market. An advantage of image processing applications using neural network technology is increased accuracy. Deep learning algorithms like convolutional neural networks (CNNs) have become the AI ​​bread and butter within SoCs. Deep learning is primarily used to solve complex problems, such as providing answers in a chatbot or a recommender function in your video streaming app. However, AI has broader capabilities that are now being used by everyday citizens.

The development of process technology, microprocessors and AI algorithms has led to the use of AI in embedded applications at the edge. To make AI easier to use for broader markets such as automotive, data centers, and the Internet of Things (IoT), a variety of specific tasks have been implemented, including facial recognition, natural language understanding, and more. But looking ahead, edge computing — and the on-device AI category in particular — is driving the fastest growth and presents the most hardware challenges when it comes to adding AI capabilities to traditional application processors.

While much of the industry is enabling AI accelerators in the cloud, another emerging category is mobile AI. The AI ​​capability of mobile processors has risen from single-digit TOPS to well over 20 TOPS in recent years. These performance-per-watt improvements show no signs of slowing down, and as the industry steadily moves closer to the point of data collection in edge servers and plug-in accelerator cards, optimization remains the top design requirement for edge device accelerators. Due to the limited processing power and memory that some edge device accelerators have, the algorithms are compressed to meet power and performance requirements while maintaining the desired level of accuracy. As a result, designers had no choice but to increase the level of computation and storage. Not only are the algorithms compressed, but given the massive amount of data that is generated, the algorithms can only focus on specific areas of interest.

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As the appetite for AI steadily increases, there has been a noticeable surge in non-traditional semiconductor companies investing in technology to cement their place in the ranks of innovation. Many companies are currently developing their own ASICs to support their unique AI software and business needs. Implementing AI in SoC design is not without many challenges.

See also: Stanford introduces new flexible AI chip

The AI ​​SoC obstacle course

The overarching obstacle to AI integration in SoCs is that design modifications to support deep learning architectures are having far-reaching implications for AI SoC designs in both specialized and general-purpose chips. This is where IP comes in; The choice and configuration of IP can determine the ultimate capabilities of the AI ​​SoC. For example, integrating custom processors can speed up the heavy math that AI applications require.

SoC designers face a variety of other challenges when balancing specific computational requirements with the implementation of deep learning capabilities:

  • Data Connectivity: CMOS image sensors for vision and deep learning AI accelerators are key examples of the real-time data connectivity needed between sensors. After compression and training, an AI model is prepared to perform tasks across a variety of interface IP solutions.
  • Security: As security breaches become more prevalent in both personal and business virtual environments, AI presents a unique challenge in securing critical data. Protecting AI systems must be a top priority to ensure user security and privacy, as well as for business investments.
  • storage performance: Advanced AI models require high-performance memory that supports efficient architectures for various memory constraints, including bandwidth, capacity, and cache coherence.
  • Specialized processing: To handle massive and changing computational demands for machine and deep learning tasks, designers implement specialized processing capabilities. With the addition of neural network capabilities, SoCs must be able to manage both heterogeneous and massively parallel computations.
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Showing the future path of AI for SoCs

To sort trillions of bytes of data and fuel tomorrow’s innovations, designers create chips that meet advanced and ever-evolving computing needs. The highest quality IP is a key to success as it enables optimizations to create more effective AI SoC architectures.

This SoC design process is inherently tedious as it requires decades of expertise, advanced simulation and prototyping solutions to tweak, test and evaluate overall performance. The ability to “maintain” the design by making necessary adjustments will be the ultimate test in determining the SoC’s viability in the market.

Machine learning and deep learning are on a strong innovation path. Expect the AI ​​market to be driven by demand for faster processing and computation, increased intelligence at the edge, and of course, the automation of more functions. Specialized IP solutions such as new processing, storage and connectivity architectures will be the catalyst for the next generation of designs that increase human productivity.


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