Automotive Graphic Processor Unit (GPU) Market Analysis (2024 - 2030)

The analysis conducted indicates that the Automotive Graphic Processor Unit (GPU) Market registered a value of USD 6.10 billion in 2023 and is forecasted to ascend to USD 45.38 billion by 2030. Projections for the period spanning 2024 to 2030 suggest a Compound Annual Growth Rate (CAGR) of 33.2%. The market's trajectory is attributed to heightened demand for autonomous vehicles, enhanced safety features, and continuous advancements in artificial intelligence (AI), the Internet of Things (IoT), deep learning, augmented reality (AR), and virtual reality (VR).

Industry Overview:

The automotive sector employs a graphics processing unit (GPU), a computer chip primarily tasked with rapid mathematical calculations to generate images. Distinguished by its numerous cores capable of managing thousands of threads simultaneously, GPUs contrast with Central Processing Units (CPUs). While standalone GPU cards possess dedicated RAM, they share main memory with CPUs in chipsets. Transistors within GPUs conduct essential mathematical operations crucial for 3D graphics. Integrated and hybrid GPUs are progressively replacing dedicated ones, finding applications in supercomputers, virtual reality and augmented reality systems, and artificial intelligence platforms. GPUs may either be integrated into video cards or embedded within the motherboard of personal computers. Various industries, including automotive, leverage GPUs to facilitate 3D content and graphics applications. For example, Computer-Aided Design (CAD) and simulation software utilize GPUs to craft realistic images or animations essential for manufacturing and design purposes. The impetus for advancing GPU technology further stems from AI, Machine Learning (ML), autonomous driving, and navigation requirements in vehicles and robotics.

Modern vehicles, particularly those deployed for autonomous driving, necessitate robust GPUs. The automotive sector has witnessed significant expansion over the last decade, with powerful GPUs and CPUs imperative for powering the artificial intelligence embedded in vehicles from manufacturers like Tesla, BMW, and Porsche. The advent of affordable yet potent electric cars marked a pivotal moment for the industry, with GPUs becoming standard equipment in modern vehicles equipped with displays. Additionally, GPUs, owing to their massively parallel processing capabilities, can optimize code execution, significantly enhancing performance compared to CPUs. Coupled with AI accelerators such as neural network accelerators (NNA), GPUs contribute to substantial power savings, crucial for electric vehicles (EVs), which dominate autonomous solutions. Given their adeptness at parallel signal processing and image analysis, GPUs are ideally suited for autonomous vehicle requirements, enabling Advanced Driver Assistance Systems (ADAS) platforms to analyze sensor data in real-time and enhance automotive transportation's speed, efficiency, and safety.

Impact of COVID-19 Pandemic on the Automotive Graphic Processor Unit (GPU) Market:

The global automotive GPU market witnessed rapid expansion in preceding years; however, the COVID-19 pandemic precipitated a sharp downturn in 2020. Government-mandated lockdowns disrupted operations across numerous sectors, including automotive, impacting the global economy. Supply chain disruptions and operational halts within the electronics sector further hampered GPU production efforts. Despite these challenges, the demand for GPUs is anticipated to rebound, driven by their pervasive use in design and engineering applications. Automotive design departments face mounting pressure to innovate swiftly, meet market demands, and expand product lines, underscoring the critical role of GPUs in this landscape.

Market Drivers:

Rising demand for autonomous vehicles and enhanced safety features propels the global automotive GPU market expansion.

Innovations in autonomous systems, necessitating rapid processing for real-time simulation to quantify uncertainty, are pivotal for their efficacy. Autonomous vehicles and Advanced Driver Assistance Systems (ADAS) offer myriad benefits including collision reduction, fuel savings, expedited travel, and enhanced accessibility. Embedded GPUs powered by AI facilitate functions like lane detection, distraction alerts, and road sign recognition, augmenting vehicle intelligence. Additionally, the adoption of 3D content and advanced graphics-intensive applications in automobiles contributes to market growth, particularly in CAD and simulation programs essential for production and design applications within the automotive industry.

Ongoing advancements in artificial intelligence (AI), the Internet of Things (IoT), deep learning, augmented reality (AR), and virtual reality (VR) drive industry expansion.

Deep learning, a subset of AI, holds promise as a key enabler for fully autonomous vehicles, leveraging powerful GPUs to analyze vast datasets in real-time. Integrating AR and VR into various applications becomes increasingly feasible with advancements in graphics technology. GPUs find utility in IoT applications, facilitating dynamic 3D user interfaces and composite processing for user interfaces.

Market Restraints:

GPUs designed for mainstream markets lack essential characteristics necessary for safe operation in automotive environments, potentially impeding market growth.

While GPUs fulfill the computational requirements of autonomous driving systems, their designs for mass-market applications may lack requisite safety features mandated by automotive regulations. Operational challenges under adverse conditions, such as sporadic hardware flaws, necessitate cautious adoption of ISO 26262 standard solutions when deploying GPUs.

Computational complexity poses challenges to market expansion.

Specialized hardware, like GPUs or tensor processing units (TPU), is requisite for AI methods like deep learning due to data volume and computational complexity. However, the speed and efficiency afforded by such hardware come with increased costs and energy consumption. Careful algorithm selection considering real-time system constraints remains imperative for optimal performance, with weaker GPUs potentially limiting new functionalities in automotive infotainment systems.

Automotive Graphic Processor Unit (GPU) Market – By Type

• Embedded
• Independent

The Automotive Graphic Processor Unit (GPU) Market is categorized into Embedded and Independent types. An embedded GPU is integrated with the CPU, eliminating the need for a separate card. Conversely, an independent GPU is a distinct chip installed on its circuit board, typically connected to a PCI Express slot. Integration of GPUs into CPUs on motherboards results in thinner, lighter systems with reduced power consumption and overall system costs. These integrated GPUs enable effective operation of various computing applications.

In contrast, independent GPUs, commonly known as dedicated graphics cards, are better suited for resource-intensive applications with high-performance requirements. While these GPUs enhance processing power, they also lead to increased energy consumption and heat generation. Typically, separate cooling mechanisms are necessary for optimal performance of independent GPUs. The demand for discrete GPUs has surged, driven by advancements in technologies like machine learning (ML) and deep learning, leading to their utilization in advanced driving assistance systems (ADAS), infotainment systems, computing-intensive servers in automobiles, and other applications.

Automotive Graphic Processor Unit (GPU) Market – By Application

• Advanced Driver Assistance Systems (ADAS)
• Autonomous Emergency Braking
• Blind-Spot Detection

• Night Vision
• Navigation Systems
• Automated Parking
• Adaptive Cruise Control
• Adaptive Headlights
• Heads-up Display (HUD)
• Pedestrian Detection/Avoidance
• Traffic Sign Recognition
• Lane Departure Warning/Correction
• Others
• Infotainment Systems
• Telematics
• Others

The Automotive Graphic Processor Unit (GPU) Market is segmented into Advanced Driver Assistance Systems (ADAS), Infotainment Systems, Telematics, and other applications. ADAS systems enhance safety through embedded vision technology, reducing the risk of accidents and injuries. These systems integrate AI functions that utilize sensor fusion to detect and process objects using vehicle cameras. Sensor fusion combines data from various sources such as image recognition software, ultrasonic sensors, lidar, and radar, enabling quick responses to stimuli. Discrete sensors like LiDAR, IR cameras, and radar facilitate functions like lane detection, pedestrian detection, and distracted driver warnings, making autonomous vehicles smarter. Deep learning algorithms aid in dynamic route determination using geo-tagging and sensor integration, ensuring accurate navigation.

ADAS systems based on GPU technology aim to mitigate human errors, which account for a significant portion of auto accidents globally. Enhanced sensors and cameras are integrated into vehicles to detect objects, with features like bird's-eye view parking assistance and heads-up displays further improving driver experience and safety. Infotainment systems offer various options to drivers, while telematics systems enable connectivity to the cloud for over-the-air updates and passenger safety.

Automotive Graphic Processor Unit (GPU) Market - By Region:

• North America
• Europe
• Asia-Pacific
• Rest of the World

Regionally, the North American Automotive Graphic Processor Unit (GPU) Market is poised to dominate, driven by key market players and increasing demand for GPUaaS from the CAD/CAM industry. In Europe, the German market is expected to witness substantial growth owing to high CAGR projections. Asia-Pacific holds a significant share in the global automotive market, supported by a strong demand for high graphic computing systems and the presence of major gaming sector players.

Key Players in the Market

• Nvidia Corporation
• AMD
• Intel Corporation
• Qualcomm Technologies, Inc.

Leading companies such as Nvidia, AMD, Intel, and Qualcomm Technologies dominate the automotive GPU market. Nvidia, renowned for its discrete graphics cards, collaborates extensively in the autonomous driving space, aiming to enhance efficiency and safety. Imagination Technologies, another significant player, focuses on developing functional automated driving systems using virtualization technology capabilities of PowerVR technology.

Recent Developments in the Automotive Graphic Processor Unit (GPU) Market:

• Product Launch: In May 2022, AMD unveiled new additions to the AMD Radeon™ RX 6000 Series product range, including the AMD Radeon RX 6950 XT, RX 6750 XT, and RX 6650 XT graphics cards.
• Product Launch: In January 2021, Qualcomm Technologies, Inc. introduced its latest digital cockpit solutions with the 4th Generation Qualcomm® SnapdragonTM Automotive Cockpit Platforms, catering to the transition toward zonal electronic/electrical (E/E) compute architecture.
• Product Launch: In June 2021, Tesla revealed an advanced AV training supercomputer powered by NVIDIA A100 GPUs, supporting Autopilot and full self-driving capabilities, offering significant performance gains over previous generations.
• Acquisition: In October 2020, AMD announced its acquisition of Xilinx, a move aimed at establishing dominance in high-performance computing and expanding product portfolio across emerging areas, with a focus on data centers, automotive, aerospace, industrial, gaming, and defense sectors.