SoC designers face a variety of challenges when it comes to balancing specific computing requirements with implementing deep learning capabilities.

Although artificial intelligence (AI) is not a new technology, it wasn’t until 2015 that a surge in new investment led to advances in processor technology and AI algorithms. Beyond simply considering it an academic discipline, the world began to take notice of this scientifically proven technology that could surpass human capabilities. Driving this next generation of investments is the evolution of AI in mainframes to embedded applications at the edge, driving a clear shift in hardware requirements for memory, processing, and connectivity in systems-on-chips. (SoC) of AI.

Over the past decade, AI has emerged to enable safer automated transportation, design home assistants tailored to individual user specifications, and create more interactive entertainment. To provide these useful functions, applications have become increasingly dependent on deep learning neural networks. Compute-intensive methodologies and global chip designs are powering deep learning and machine learning to meet the demand of everything smart. Silicon-on-chip technology must be able to provide advanced mathematical functions, powering unprecedented real-time applications such as facial recognition, object and voice identification, and more.

Defining AI

Most AI applications follow three fundamental elements: perception, decision-making and response. By using these three basic elements, the AI ​​has the ability to recognize its environment, use the information from the environment to inform itself and make a decision, and then, of course, to act accordingly. 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 includes the machine’s ability to solve a problem when faced with an unfamiliar task. Weak AI makes up most of the current market, while strong AI is seen as a forward-looking goal that the industry hopes to utilize in the coming years. While both categories will bring exciting innovations to the AI ​​SoC industry, strong AI opens up a plethora of new applications.

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Machine vision applications are a driving catalyst for new investments in AI in the semiconductor market. One benefit of machine vision applications that use neural network technology is increased accuracy. Deep learning algorithms such as convolutional neural networks (CNNs) have become the bread and butter of AI within SoCs. Deep learning is mainly used to solve complex problems, such as providing answers in a chatbot or a recommendation function in your video streaming application. However, the AI ​​has broader capabilities that are now harnessed by ordinary citizens.

The evolution of process technology, microprocessors and AI algorithms has led to the deployment of AI in embedded applications at the edge. To make AI more user-friendly for broader markets such as automotive, data center and Internet of Things (IoT), various specific tasks have been implemented including face detection, language understanding natural, etc But going forward, edge computing — and specifically the on-device AI category — is driving the fastest growth and posing the most hardware challenges in adding AI capabilities to traditional application processors.

While much of the industry is activating cloud-based AI accelerators, another emerging category is mobile AI. The AI ​​capability of mobile processors has grown from single-digit TOPS to over 20 TOPS in the past few years. These performance-per-watt improvements show no signs of slowing down, and as the industry gradually approaches the point of data collection in edge servers and plug-in accelerator cards, optimization continues to be the primary requirement for design for edge device accelerators. Due to the limited computing power and memory available to some edge device accelerators, the algorithms are compressed to meet power and performance requirements, while preserving the desired level of accuracy. As a result, the designers had no choice but to increase the level of calculation and memory. Not only are the algorithms compressed, but given the huge amount of data generated, the algorithms can only focus on designated areas of interest.

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

See also: Stanford unveils new flexible AI chip

The AI ​​SoC Obstacle Course

The major barrier to integrating AI into SoCs is that design changes to support deep learning architectures are having a huge impact on AI SoC designs in specialty and general-purpose chips. . This is where intellectual property comes into play; the choice and configuration of IP can determine the final capabilities of the AI ​​SoC. For example, the integration of custom processors can accelerate the deep calculations required by AI applications.

SoC designers face a variety of other challenges when balancing specific computing requirements with implementing 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. Once compressed and trained, an AI model will be prepared to perform tasks through a variety of interface IP solutions.
  • Security: As security breaches become more common in personal and business virtual environments, AI offers a unique challenge in securing important data. Protecting AI systems should be a top priority to ensure user security and privacy as well as business investments.
  • Memory performance: Advanced AI models require high-performance memory that supports efficient architectures for different memory constraints, including bandwidth, capacity, and cache coherency.
  • Specialized treatment: To handle massive and changing computational needs for machine and deep learning tasks, designers implement specialized processing functions. With the addition of neural network capabilities, SoCs must be able to handle both heterogeneous and massively parallel computations.

Charting the Future Path of AI for SoCs

To sort through trillions of bytes of data and power the innovations of tomorrow, designers are developing chips capable of meeting ever-changing advanced computing demands. Higher quality IP is one of the keys to success, as it enables optimizations to create more efficient AI SoC architectures.

This SoC design process is inherently arduous as decades of expertise, advanced simulation, and prototyping solutions are required to optimize, test, and benchmark overall performance. The ability to “nurture” the design with necessary customizations will be the ultimate test in determining the viability of the SoC in the market.

Machine learning and deep learning are on the path to innovation. It is safe to anticipate that the AI ​​market will be driven by demand for faster processing and calculations, increased intelligence at the edge, and of course, automation of more functions. Specialized IP solutions such as new processing, memory and connectivity architectures will be the catalyst for the next generation of designs that improve human productivity.

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