New-Tech Europe Magazine | Q2 2021

Figure 3: An autonomous vehicle verification and validation environment must consider sense, compute, and actuate. (Image source: Siemens EDA)

wheel, such as Siemens AMESim, and others. AI/ML AI/ML designs record the largest transistor count driven by new architectures for computing, storage, and memory access. New architectures target specific

system, calling for integration of several technologies. Hardware emulation computes the sensor data generated by a virtual environment like VECTOR CANoe, dSPACE, or Siemens Pre-Scan, and generates actions to be sent to actuate implementation via functional models for the engine and steering

same stimulus and verification setup from end to end. Autonomous driving AD designs involve several critical issues from safety and security concerns to avoid liabilities to big data processing that require massive communication between vehicle and cloud computing. Verification challenges stem from the growing number of sensors that may exceed 50 various types, the increasing amount of software, now reaching 100 million lines of code, and the hardware and software complexity that must be validated together. This requires a vast amount of verification cycles to certify that an autonomous driving car is safe and secure. Hardware Emulation for AD Verification/validation of an AD controller must deal with sense, compute, and actuate. Sense collects sensory information to capture driving scenarios. Compute performs algorithmic processing of those scenarios to formulate a decision. Action acts on those decisions by sending commands to the engine, transmission, steering and braking

Figure 4: Implementing a computational storage device can eliminate some bottlenecks to improve performance, lower power, and free up PCIe bandwidth. (Image source: Siemens EDA)

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