Autonomous systems and physical AI are moving toward radical transformations in multiple sectors such as transportation, robotics, energy, and manufacturing. However, the actual deployment of these systems encounters overlapping technical obstacles that extend from sensors to processing and execution units, prompting chip and semiconductor system designers to comprehensively rethink hardware architecture.
Market forecasts indicate significant growth, with estimates from the consulting firm Strategy&, part of PwC, suggesting that the global physical AI market could reach approximately 430 billion euros by 2030. Despite this commercial momentum, industry experts emphasize that increasing computing power alone is not enough to solve existing bottlenecks; rather, processing efficiency must be aligned with memory bandwidth, effective interfaces, available power, and heat dissipation systems.
The Shift Toward Specialized Architectures
Overcoming system-wide bottlenecks requires highly specialized hardware and software designs optimized specifically for the targeted use cases. In this context, executives in the security and chip solutions sector believe that the industry is gradually returning to Application-Specific Integrated Circuits (ASICs) designed precisely to fit the required workload, especially in light of the rapid development of AI models.
Autonomous robots and self-driving cars share core technologies such as perception and control, yet each application imposes unique challenges. Cars operate within an organized traffic infrastructure and draw power from large batteries, while mobile and humanoid robots face unstructured three-dimensional environments that require maintaining balance and manipulating objects amidst extremely limited power budgets.
The Memory Crisis and Bottleneck
Memory capacity, bandwidth, latency, and power consumption represent major bottlenecks for physical AI systems. As the number of parameters in AI models increases, there is an urgent need for ultra-high-speed interfaces such as LPDDR6 technologies to ensure that Random Access Memory (DRAM) does not become an obstacle that hinders processing.
Non-volatile storage also plays a pivotal role in reducing model loading times and managing space constraints and power consumption in devices that rely on batteries and lack active cooling. Engineers are seeking to balance the reliance between on-chip cache memory (SRAM), external DRAM, and fast storage media to achieve optimal efficiency within the permitted thermal and power envelope.
Reliability Verification and Safety Standards
Establishing verification loops and rigorous testing before bringing physical AI systems to market is crucial, especially when integrated into heavy machinery and safety-critical applications. The matter is not limited to passing tests at the individual component level; it has extended to the necessity of evaluating the entire system's response in the event of sensor failures, communication interruptions, or exposure to unexpected environments.
Specialists point out that relying on standardized, reliable, and low-power interfaces, such as the MIPI camera security framework, allows designers to build integrated and proven blocks that ensure data protection and integrity from the sensor to the CPU, which helps balance loads and reduce power consumption and latency without compromising critical safety requirements.