The rise in the use of artificial intelligence (AI) over the past two and a half years has radically changed not only software but also hardware. As the use of AI continues to evolve, PC manufacturers have found an opportunity in AI to improve end-user devices by offering AI-specific hardware and marketing them under the name of “PC AI”.
Pre-AI hardware, fit for AI
A few years ago, AI often depended on hardware that was not explicitly designed for AI. An example is graphics processors. Nvidia graphics processing units (GPUs) are crucial in AI because they efficiently handle parallel processing, which is necessary for machine learning and deep learning. Their design allows for simultaneous calculations, making them more efficient than CPUs for training and inferring AI models.
Another main type of hardware is the Field-Programmable Gate Array (FPGA) from Intel and other companies. An FPGA is an integrated circuit (IC) that can be reprogrammed multiple times. This flexibility makes it ideal for AI tasks. FPGAs accelerate deep learning and machine learning tasks. They provide hardware customization options that mimic the behavior of GPUs or ASICs.
FPGAs can be integrated with popular AI frameworks such as TensorFlow and PyTorch using tools such as Intel FPGA AI Suite and the OpenVINO toolkit.
FPGAs are used in the automotive, healthcare, and other industries. They are useful in edge computing scenarios where AI capabilities need to be deployed close to the data source for faster decision-making and reduced latency.
And yet another type is application-specific integrated circuits (ASICs). One example is Google’s Tensor Processing Units (TPUs). TPUs are custom ASICs developed by Google to accelerate machine learning workloads. They are optimized for TensorFlow and widely used in Google data centers.
How the generative AI revolution changed hardware
The release of ChatGPT by OpenAI on November 30, 2022 has changed the public and industry’s relationship with AI. ChatGPT quickly gained immense popularity, attracting over a million users within five days of its release. In January 2023, it had reached 100 million usersmaking it the fastest growing consumer app ever.
More importantly, ChatGPT’s runaway success in the mainstream has shifted venture capital funding in favor of AI startups. Major tech giants like Microsoft, Google, and Meta accelerated the development and public availability of their offerings, and Silicon Valley quickly saw the emergence of companies like Anthropic and Perplexity. AI tools.
Now, PC makers are investing in AI-enabled PCs that emphasize hybrid AI and in-device intelligence. The integration of AI into personal computers is being facilitated by the emergence of specialized AI chipsets, such as neural processing units (NPUs), which enhance the ability of PCs to perform AI tasks locally.
This change is expected to have a significant impact on the PC market. Around 60% of PCs shipped by 2027 will be AI-enabled, according to Canalys.
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How do AI PCs differ?
AI PCs are designed to efficiently run AI workloads using a combination of CPU, GPU and NPU, allowing them to handle tasks such as Generative AI models more efficiently than previous generations of PCs. This optimization allows AI PCs to run AI applications with improved performance, energy efficiency and privacy by processing data locally rather than relying on cloud-based solutions.
Some criticize this category as a marketing gimmick and point out that many end users use generative AI through cloud-based chatbots.
Today, the public views AI as large language models (LLM) running in the cloud and used as chatbots. Over time, the power and use of AI by end users will increasingly come through integrated features and AI-enhanced applications.
According to Chris Howard, global head of research at GartnerAI will also involve more small language models (SLMs) powering non-chatbot use cases operating near the edge rather than the cloud.
AI processing will happen closer and closer to the user and the periphery. And that means the trend toward AI-specific hardware will only continue to grow.
Microsoft AI
One of the highlights is the introduction of Microsoft’s Copilot+ PCs, a new category of Windows PCs designed specifically for AI. These PCs feature new silicon capable of more than 40 trillion operations per second (TOPS), providing all-day battery life and access to advanced AI models. The architecture of these devices integrates a high-performance NPU alongside the CPU and GPU, enhancing their AI capabilities. This setup enables new experiences like real-time AI image generation, language translation, and advanced search features like “Reminder,” which records and analyzes device activity to improve interaction of the user with AI models.
Microsoft has also collaborated with major OEM partners including Acer, ASUS, Dell, HP, Lenovo and Samsung to bring these AI-enhanced devices to market.
Apple AI
Apple has made several hardware changes to integrate and strengthen the AI capabilities of its devices. An important development is the integration of Apple silicon, specifically designed to handle advanced AI processing. This includes the use of specialized neural engines in devices like the iPhone 15 Pro, which are optimized for AI tasks such as machine learning and natural language processing. These neural engines improve the efficiency and speed of AI operations, enabling features such as real-time language translation and image recognition.
Google AI
Google has made several changes to its hardware to accommodate AI. One important step is to develop and integrate its own hardware to support AI models like Gemini. This indicates a shift from using external chips to using proprietary technologies to improve AI capabilities.
Google has even reorganized its internal teams to better integrate AI into its products. This reorganization led to the creation of a new Platforms and Devices team, bringing together various Google products like Pixel, Android, Chrome and ChromeOS under a single leadership. The move aims to accelerate AI integration and improve synergy between hardware and software.
The AI hardware revolution
The popular generative AI revolution began in November 2022 and has brought significant hardware changes to address power-intensive AI use cases. The recent adaptation of AI to hardware is undoubtedly just the beginning. We can expect AI-specific hardware to expand beyond PCs and phones, to wearables, Internet of Things devices and more.
