Why co-design of hardware, drivers, and application software is essential for cost-effective production solutions for embedded applications.

In large, mature industries like automotive, companies naturally develop silos of expertise in areas such as hardware, software, and system validation. These silos exist not only within OEMs and Tier 1 suppliers but also among semiconductor vendors and third-party providers. Integrating these specialized teams to create cohesive, validated solutions is a critical management challenge in developing complex automotive subsystems. For embedded systems, this task is further complicated by strict cost, thermal, power, and reliability constraints essential for production. In safety-critical applications like self-driving, the stakes are even higher: ensuring safety without compromising embedded and production requirements is vital.

The unique demands of AI-driven automotive systems

Automotive systems differ fundamentally from general-purpose products like PCs or smartphones. Each subsystem is finely optimized for specific task to ensure functionality, reliability, and safety at an affordable cost. This efficiency is why OEMs have historically relied on in-house development or close collaboration with specialized Tier 1 suppliers. 
AI-driven applications for self-driving systems present unique challenges. These applications require immense computational power and stringent safety measures to ensure that vehicles protect both occupants and pedestrians under all conditions. Yet, these being ‘under the bonnet’ features, consumers are often reluctant to pay significantly more for advanced features like L2 and L3 autonomy. As a result, manufacturers must deliver these capabilities cost-effectively without compromising reliability.

The role of hardware-software co-design in efficiency

Software engineers need a deep understanding of the hardware platform to develop efficient compute-intensive AI applications. Optimizing software for specific hardware can achieve performance gains of 2x to 10x or more, as measured by aiMotive. These optimizations can reduce system costs by 50–75% across a vehicle portfolio while lowering power consumption to address thermal and reliability demands. Achieving such efficiencies requires closer collaboration between hardware and software teams than in general-purpose systems.

Embedded AI systems blur the boundaries between low-level software, middleware, and application software. Any inefficiencies in these layers can drastically reduce performance. Modern NPUs (Neural Processing Units) address this by providing predefined libraries for common AI functions. However, optimizing performance requires tight integration with application code and hardware-specific adjustments. Without this collaboration, processing resources are wasted, or the NPU stalls while waiting for other chip components to synchronize. 

Co-design enables continuous iteration from high-level application software down to low-level hardware, delivering significant gains in cost, performance, and safety metrics.

Overcoming validation and integration challenges

Co-design benefits validation, too. Given the stringent safety requirements for automated driving systems, validation of AI-based solutions becomes one of the most significant costs of delivering a safety-critical solution. To help reduce validation costs, hardware and software teams working closely together can better devise smarter approaches to testing software, developing tailored solutions that help with scaling up the compute demands for processing thousands of hours of sensor data cost-effectively.  
Challenges intensify when hardware and software teams are siloed across organizations or locations. Intellectual property concerns often hinder collaboration, further complicating integration efforts. Inefficient co-design can result in higher development costs, potentially inflating them by 3–5 times. For programs costing hundreds of millions, this inefficiency can threaten profitability for OEMs.

aiMotive’s approach to seamless integration

Recognizing these challenges, Laszlo Kishonti founded aiMotive to address the extreme efficiency demands of AI-driven self-driving systems. Drawing from his experience in graphics and benchmarking for mobile devices, Kishonti emphasized the importance of software-hardware co-design. Without this integration, AI-driven autonomy would remain impractical for mass production.

aiMotive also took inspiration from the collaboration between GPU vendors and game developers, which has enabled remarkable performance in modern devices. Understanding the importance of tools, aiMotive even developed leading-edge simulation and training data tools that perfectly complement aiWare and aiDrive technologies. By assembling multidisciplinary teams encompassing chip design, operating systems, drivers, compilers, AI algorithms, application development, and system validation, aiMotive fosters seamless collaboration under one roof. These platform-neutral solutions allow semiconductor vendors, OEMs, and Tier 1 suppliers to build cost-effective, high-performance systems for L2 and L3 autonomy while meeting strict functional safety standards. 

As a dedicated automotive self-driving technology solutions provider, only aiMotive has all the skills needed under one roof to create customized, scalable, production-ready solutions for its customers.

As the automotive industry progresses toward greater autonomy, aiMotive’s comprehensive expertise positions it as a unique partner thanks to its comprehensive skills in hardware, software, tools, and AI algorithms. Regardless of market dynamics or economic shifts, aiMotive’s innovative solutions help realize the dream of safe, efficient, and autonomous vehicles.