Author: Deepak Shankar, Vice President Technology, Mirabilis Design Inc.
Introduction
The rapid adoption of autonomous driving technologies and electric vehicles (EVs) has fundamentally shifted automotive electronics design. As AI-based functionalities proliferate across vehicle subsystems, system architects face increasing complexity in balancing performance, power efficiency, and hardware costs. Traditional design approaches relying on spreadsheets and analytical models no longer suffice to predict system behavior and performance accurately.
This paper explores the unique challenges of implementing AI workloads in automotive platforms and presents system-level modeling as a powerful methodology to address design complexity, optimize compute resource utilization, and achieve performance-power balance.
The Impact of AI on Automotive Design
AI-driven functions such as perception, path planning, driver monitoring, and predictive maintenance require extensive real-time computation. These AI models, built on deep neural networks (DNNs), involve multiple processing layers with massive memory and compute requirements. Shifting from cloud-based to edge-based AI execution is essential to reduce latency and maintain power budgets within automotive constraints.
Unlike conventional computing, AI inference heavily stresses memory subsystems due to repeated operand fetching and result storage. An 8-bit adder operation may consume as little as 0.03 picojoules, while accessing 32 bits from DRAM can consume over 640 picojoules. As transistor efficiency plateaus, memory access energy dominates system power consumption.
Moreover, AI data centers have raised broader sustainability concerns, with large-scale models generating substantial carbon footprints. Automotive designs, with strict thermal envelopes, demand much more efficient hardware architectures.
Understanding Performance Bottlenecks
A common misconception in AI hardware design is to focus solely on increasing the number of cores. In reality, the primary bottlenecks lie in the memory subsystems and interconnect fabrics that shuttle data between compute elements and memory. Network-on-Chip (NoC) and memory controller efficiency significantly influence overall performance.
Simulation studies demonstrate that increasing multiply-accumulate (MAC) operations can lead to unpredictable latency swings if memory and interconnect bandwidth are not adequately provisioned. High core counts without efficient data movement lead to diminishing performance returns.
System-Level Modeling: A New Design Paradigm
System-level modeling enables designers to evaluate architecture options early in the development cycle. By simulating workloads across heterogeneous compute resources, engineers can:
- Evaluate processor types (ARM, RISC-V, x86)
- Assess AI tile and GPU utilization
- Determine optimal workload partitioning across processing units
- Explore memory hierarchies and bandwidth configurations
- Analyze power profiles dynamically
The methodology involves creating functional models of the system using component libraries, assigning workloads (task graphs), and defining key performance metrics such as latency, power, memory utilization, and throughput. Regression analysis across parameter sweeps allows architects to visualize trade-offs and select optimal configurations before hardware implementation.
Case Study: AI Accelerator Mapping
In one example, a heterogeneous platform using AMD Versal SoCs was modeled to explore AI inference optimization. These SoCs integrate AI engines, programmable logic, general-purpose processors, and memory controllers into a single package.
Key findings from this study include:
- Initial designs relying solely on logic gates and processors resulted in significant data overflow and rising latencies.
- Shifting workloads to dedicated AI engines stabilized performance and reduced inference time but increased average power consumption slightly.
- Implementing direct memory connections bypassing the NoC yielded flatter latency profiles while raising average power further.
These iterative modeling cycles enabled designers to identify balanced configurations tailored to specific AI workloads such as ResNet-50.
The Rise of Chiplet Architectures
Emerging chiplet-based designs offer additional flexibility in managing heterogeneous computing needs. Chiplets, akin to modular Lego blocks, allow designers to assemble custom system-on-chip (SoC) solutions by combining specialized compute and memory dies.
Standardized interfaces such as Universal Chiplet Interconnect Express (UCIe) enable multi-vendor interoperability and foster ecosystem development. Chiplets can significantly reduce development cost and time-to-market by enabling rapid customization for varying automotive applications.
VisualSim: Enabling System Exploration
VisualSim from Mirabilis Design provides a comprehensive platform for system-level modeling, allowing automotive engineers to:
- Build detailed functional models incorporating processors, AI accelerators, memory, interconnects, and workloads.
- Run exhaustive regression tests to analyze architectural trade-offs.
- Validate system specifications, task graphs, and scheduling policies prior to hardware commitment.
- Perform power analysis, failure analysis, and safety assessments using the same system model.
By integrating software task graphs with hardware resource models, VisualSim enables collaborative design among hardware, software, and AI teams, streamlining complex development workflows.
Conclusion
AI is no longer a future concept but a present-day reality driving automotive innovation. As vehicle architectures grow increasingly complex, system-level modeling becomes indispensable for managing heterogeneous compute resources, optimizing memory usage, and balancing performance with power and cost constraints.
System-level design tools such as VisualSim empower automotive developers to confidently explore design alternatives, ensure efficient workload mapping, and deliver robust, scalable electronics platforms for the autonomous and electric vehicles of tomorrow.
