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Over the past decade, artificial intelligence has matured from a laboratory curiosity to a pervasive technology applied to everything. The AI market is poised to reach $1.6 billion by 2030, up from $87 billion in 2021, according to Precedence Research. This explosive growth rests on a foundation of semiconductor innovation, from the AI systems-on-chip that perform parallel data processing at high speeds to the AI tools used to design and fabricate those chips.
Here are four trends in artificial intelligence that promise to this year drive this innovation and help address some of the core challenges the industry is facing.
Companies will simplify the integration of heterogeneous components
Performing more AI processing at the edge promises to steer the focus of much effort and innovation this year to integration. For edge devices, the artificial-intelligence system needs to perform a variety of different tasks, which requires not only different types of compute capabilities but different types of memory, connectivity and sensor input. Building systems that integrate heterogeneous components in a functional, power-efficient and manufacturable design present a significant challenge. The effort must encompass other engineering domains, such as mechanical design, optical design, electrical design and both digital and analog semiconductor design.
Integration in the data center presents a different but still very diverse set of challenges. To deliver the deep compute performance required, multiple die need to be integrated into a single silicon device. These components comprise mostly dense digital logic, like the raw compute horsepower needed to accelerate large neural networks.
Adoption of AI design tools will grow another 10×
As chip and fabrication complexity continue to rise, the adoption of AI design tools is growing exponentially. In just one year, the number of commercial chips designed by artificial intelligence has increased by at least an order of magnitude. As the proliferation of AI design technology accelerates, training datasets becomes more comprehensive and design teams begin to leverage the strengths of the new breed of tools.
As the technology matures over the coming year, new design capabilities driven by artificial intelligence will deliver productivity breakthroughs in new areas of chip design and emerge to help create more complex designs to meet power, performance and area demands. Don’t be surprised to see AI reinforcement-learning–based applications used to solve various design challenges reach market this year.
Generative AI will speed application development
One of the most challenging and time-consuming aspects of developing a new AI application is the process of creating a model and then optimizing and training it to perform a specific task. This has given birth to more research into what are known as foundation models.
A foundation model is an AI model that you design once and then train using very large datasets to achieve various objectives. Once trained, the model can be adapted to many different applications. The goal is to spend less time architecting and engineering a new model specifically for each application. The sheer scale of foundation models allows users to achieve entirely new capabilities that were previously unattainable.
Foundation models are driving another AI evolution that will enter center stage this year: generative AI. This new wave of artificial intelligence focuses on creating new content, based on underlying models’ ability to train on very large bodies of work, including text, images, speech and even 3D signals from sensors. Based on the input, the same foundation model can be trained to synthesize new content, such as creating art, creating music or even generating text for chatbots. Generative AI will make creating new content breathtakingly easy.
AI will be instrumental in the pursuit of net zero
AI design tools can directly help our global efforts toward net zero—a state of net-zero carbon dioxide emissions also known as carbon neutrality—by better optimizing AI processor chips for energy efficiency. According to an informal study that Synopsys recently conducted, optimizing an AI chip with AI design tools delivered, on average, about 8% energy savings across the board. Now apply that 8% savings to every data center in the world. That represents an enormous amount of energy.
We see the benefits going beyond chip design — think optimizing the nation’s energy grid performance, maximizing crop yield or minimizing water consumption. Artificial intelligence can both reduce its carbon footprint and compensate by bringing benefit to the environment.
Over the next year, advances in AI tools and functions will support innovation across the industry and provide easier access for new entrants while leveling the playing field for smaller enterprises.
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