Infineon AURIX TC4x Parallel Processing Unit (PPU)

The Parallel Processing Unit (PPU) in Infineon’s AURIX™ TC4x microcontroller family is a specialized co-processor designed to accelerate highly parallel computations for automotive and industrial applications. It complements the TC4x’s TriCore™ 1.8 CPUs by offloading computationally intensive tasks such as digital signal processing and neural network inference, enabling artificial intelligence (AI) capabilities up to the highest automotive safety levels (ASIL-D). The PPU features a vector processing architecture and dedicated hardware accelerators, allowing it to execute math-intensive algorithms in parallel and in real time, which is critical for advanced driver assistance systems (ADAS), electric powertrain control, and other safety-critical functions.

Overview

Infineon’s AURIX™ TC4x is the third-generation family of 32-bit automotive microcontrollers, built for safe and secure real-time processing in domains such as powertrain, chassis, advanced safety, and ADAS. The TC4x family integrates up to six TriCore™ v1.8 CPU cores (with lockstep redundancy for safety) and a suite of specialized accelerators to meet the increasing performance demands of modern vehicles. Among these new accelerators is the Parallel Processing Unit (PPU) – a new programmable vector co-processor introduced in the TC4x family to boost signal processing and AI performance. The PPU’s role is to handle tasks that involve large amounts of numeric computation or data parallelism, thereby augmenting the microcontroller’s throughput while maintaining deterministic real-time behavior needed for automotive applications.

Designed as a heterogeneous multi-core architecture, the AURIX™ TC4x combines conventional scalar processing cores with the PPU vector core and other accelerators. This approach allows the system to offload specialized workloads to the appropriate processing unit. For example, time-critical control tasks and general application code run on the TriCore CPUs, while the PPU is invoked for high-volume data processing like sensor data filtering, complex control algorithms, or neural network evaluation. By relieving the main CPUs of these heavy workloads, the PPU helps the TC4x family achieve significantly higher performance (up to 78× in certain benchmarks compared to a single TriCore 1.8 core) without compromising the strict safety and real-time requirements of automotive systems.

Architecture

Core Design and Instruction Set

The PPU is implemented as a vector processor based on the Synopsys DesignWare ARC EV71 architecture. It consists of a 32-bit RISC processing core coupled with a wide Single-Instruction Multiple-Data (SIMD) vector unit. The PPU’s vector unit features 512-bit vector registers and executes instructions that operate on multiple data elements in parallel. In essence, a single vector instruction can perform the same operation across an entire array of values (for example, multiplying 16 or more data points at once), which contrasts with the scalar TriCore CPUs that execute one operation on one set of operands at a time. This vectorized design, common in digital signal processors (DSPs), enables significantly higher throughput for algorithms that can be parallelized, such as signal filtering, matrix operations, and image processing.

Memory Model and Parallelism

The PPU operates within the microcontroller’s memory space as a peer processor alongside the TriCore CPUs. It is equipped with its own local memory hierarchy (including instruction and data caches or tightly coupled memories) and connects to the shared memory via the on-chip interconnect fabric. A Data Routing Engine (DRE) is included in the TC4x architecture to facilitate efficient data movement between the PPU, main memory, and other peripherals. This helps feed the PPU with data (for example, sensor readings or large data buffers) and retrieve results with minimal CPU intervention. The PPU can also use direct memory access (DMA) to autonomously fetch and store data to shared memory, ensuring that data transfers occur in parallel with computation.

To support parallel processing without interfering with the real-time tasks on the TriCore cores, the PPU and CPU cores communicate through well-defined mechanisms. Shared memory regions and mailbox registers allow exchange of data and signals between the scalar cores and the PPU. Inter-core interrupts are used for synchronization and job control – for instance, a TriCore core can signal the PPU to start processing a dataset, or the PPU can interrupt a CPU when it has finished a computation task. This architecture enables true parallelism at the system level: while the PPU crunches numbers on a vectorizable task, the main CPUs can continue executing other software tasks. The net effect is a form of coarse-grained parallel processing (different cores executing different tasks concurrently) combined with the fine-grained data parallelism within the PPU itself. The PPU’s vector unit executes SIMD operations across up to 512-bit wide data sets, and its internal design can be configured to handle various data widths (such as 8, 16, or 32-bit elements) in parallel. This flexibility allows tuning for either higher precision or higher parallel count, as needed by the application.

Hardware Acceleration and Applications

Illustrative range of PPU application domains and tasks. The PPU can handle high-frequency control loops (on the order of microseconds) for power electronics as well as more complex, slower tasks such as sensor fusion and AI-based predictive control. It supports both observer-based control algorithms and neural network–based functions across automotive use cases

The PPU provides significant hardware acceleration for computations common in automotive and industrial systems. Its vector DSP engine is capable of high-throughput signal processing. In practical terms, the PPU can execute complex math such as fast Fourier transforms, digital filters, matrix multiplications, and trigonometric calculations much faster than the general-purpose TriCore CPUs by leveraging its 512-bit SIMD instructions. This is especially beneficial for applications like radar signal processing or sensor data fusion, where large matrices or arrays of data must be processed under tight time constraints.

The automotive applications of the PPU span a wide range of domains:

• eMobility and power control: In electric vehicles, the PPU can be used in on-board chargers, DC/DC converters, and traction motor inverters to perform high-bandwidth control algorithms and complex calculations for power conversion efficiency and motor control. For example, field-oriented control of motors involves heavy linear algebra and trigonometric computations that the PPU can accelerate. It also enables advanced battery management system (BMS) functions like state-of-charge (SoC) and state-of-health (SoH) estimation using adaptive algorithms or even neural networks.
• Advanced Driver Assistance Systems (ADAS): The PPU supports use cases in radar signal processing, lidar, and sensor fusion for ADAS. It can process raw data from radar sensors using fast DSP operations or run a neural network to identify objects in sensor data, all within the tight latency required for functions like automatic emergency braking or lane-keeping. Its high parallel throughput is advantageous for handling the massive data streams from high-resolution sensors in real time.
• Domain and zone controllers: Future vehicle E/E architectures often involve domain controllers (for vehicle dynamics, chassis control, etc.) or zonal controllers that handle multiple functions. The PPU is suited for domain control tasks such as predictive vehicle motion control, complex vehicle dynamics simulations, or coordinating multiple sensor inputs. It enables these controllers to implement sophisticated algorithms (like model-predictive control or AI-based sensor calibration) that require intensive computation, thereby increasing the accuracy and responsiveness of systems like stability control or autonomous driving logic.
• Safety and monitoring features: Even tasks like siren sound detection (acoustic pattern recognition for emergency vehicle detection) or other audio signal processing in the vehicle can leverage the PPU’s DSP capability. Similarly, the PPU can assist with cybersecurity or functional safety monitoring algorithms that may use heavy mathematics (for example, cryptographic filtering or redundancy checks) by accelerating those computations in parallel.

While the AURIX™ TC4x and its PPU are primarily aimed at automotive, many of these capabilities are equally valuable in industrial applications. Industrial control systems (such as robotics controllers, industrial drives, or renewable energy inverters) have similar demands for real-time, high-throughput computations. The PPU’s hardware acceleration of control algorithms and neural network inference can enable smarter factory automation, high-performance motor drives, and safety systems in industrial settings. Furthermore, the compliance of the TC4x platform with automotive safety standards (ISO 26262 ASIL-D) corresponds to SIL 3 capability under IEC 61508 for industrial use, making the PPU-equipped microcontrollers attractive for safety-critical industrial controllers as well.

Integration with the TC4x Platform

The PPU is tightly integrated into the AURIX™ TC4x system-on-chip alongside the TriCore CPU clusters and other accelerators. It functions as a co-processor, with a level of autonomy in executing its own instruction stream, yet it shares the overall memory map and resources of the microcontroller. The integration is designed such that the PPU can be treated as another compute core in the system, managed by the system software when needed. For example, the TC4x platform includes an AUTOSAR-compatible Complex Device Driver (CDD) for the PPU, which allows automotive software (running on a TriCore) to dispatch tasks to the PPU and manage its operation in a controlled manner. A runtime component often called the PPU dispatcher is provided to queue and schedule parallel tasks on the PPU, handle the initiation of PPU execution, and retrieve results when finished. This dispatcher abstracts the details of PPU job control from the application, so developers can request computations (like “perform this FFT” or “run this neural network on new data”) and the system will utilize the PPU to complete them asynchronously.

From a hardware perspective, the PPU connects to the microcontroller’s internal buses and interconnects. Shared SRAM memory is accessible to both the TriCore cores and the PPU, enabling bulk data to be passed by reference rather than copied between cores. For instance, a TriCore core can populate a buffer with sensor data in shared memory and then signal the PPU to process it, rather than explicitly feeding each data point. The Data Routing Engine (DRE) further assists in shuttling data between the PPU and other subsystems efficiently. In addition, mutual exclusion and memory protection mechanisms ensure that the PPU’s operations do not interfere with the timing and memory of the main CPUs. Infineon’s architecture implements safeguards so that even though the PPU shares buses and memory, critical real-time tasks on the TriCore (such as an interrupt service routine for safety) can preempt bus access if needed to maintain determinism (this falls under the TC4x’s overall freedom-from-interference design philosophy for mixed-criticality systems).

Inter-core communication is achieved through interrupts and handshaking flags. The TriCore CPUs can start or stop the PPU, and the PPU can interrupt the main CPUs upon task completion or if it needs attention. Software mailboxes (basically designated memory or register locations) are typically used to post job descriptors or status flags between the cores. This design is similar to a heterogeneous multi-processor system where a host CPU controls an accelerator: the host sets up the data and parameters for the accelerator, triggers it, and later reads back the results. In TC4x, however, all of this happens on a single chip and within a unified development environment, making the use of the PPU relatively seamless for developers familiar with multi-core programming.

Notably, the PPU is independent of the TriCore CPU architecture – it does not execute TriCore instructions and vice versa. Instead, it runs its own code (compiled for the ARC EV71 ISA) from either internal code memory or system memory. Tools like debuggers have been updated to be aware of this extra core. For example, Lauterbach’s TRACE32 debugging tool can simultaneously debug all TriCore CPUs and the PPU and trace their execution in parallel. This full-system visibility is important when integrating PPU tasks into the application, since developers need to coordinate and verify the interaction between the main application and the parallel routines on the PPU.

Real-Time and Safety Considerations

A critical aspect of the AURIX™ TC4x PPU is that it is designed to meet the stringent real-time and functional safety requirements of automotive systems. In terms of real-time behavior, the PPU’s operations are deterministic and can be analyzed for worst-case execution time, which is essential for ensuring it fits within the timing budgets of safety-critical tasks. The use of hardware acceleration means that tasks that would otherwise take an impractically long time on a CPU (potentially causing deadline misses) can be completed much faster on the PPU, often turning minutes of CPU processing into milliseconds or less. This allows sophisticated algorithms (like high-order filters or deep neural networks) to be used in real-time control loops where previously they would have been too slow. System designers can assign PPU-heavy tasks lower priorities or schedule them in parallel so that the main control loop on a TriCore is never delayed waiting for the PPU; instead, results are ready when needed. The TC4x architecture also supports features like CPU and bus virtualization to ensure that even when multiple cores (TriCore and PPU) are active, critical tasks maintain their timing (for example, through quality-of-service controls on memory accesses).

From a safety perspective, Infineon has built the PPU and TC4x as ASIL-D compliant components, meaning they can be used in systems that require the highest level of automotive safety integrity. The PPU hardware is likely implemented with various safety mechanisms: its internal memories (register files, caches) and buses have error-correction (ECC) to detect and correct bit flips; the logic may have built-in self-test routines and fault diagnostics that run at startup or periodically to ensure the PPU is operating correctly. Infineon and Synopsys also offer a functional safety variant of the ARC EV processor (EV71FS) which would include safety extensions such as lockstep comparators or redundant computation for critical parts. These measures enable the PPU to detect internal faults and either correct them or report them to the safety monitors in the system, so that a proper safe state can be achieved if a malfunction occurs. The overall TC4x microcontroller includes a safety management unit that supervises all cores (TriCore and PPU alike) and can, for example, reset or isolate a core that behaves unexpectedly.

The SAFE AI initiative is an example of how the PPU’s capabilities are being qualified for safety. In 2024, Fraunhofer IKS assessed the AURIX™ TC4x family with its PPU for the safe deployment of AI in automobiles. The result was that the PPU, as an AI accelerator, meets the necessary safety and robustness criteria for using machine learning in safety-critical systems. This is significant because AI algorithms (like neural networks) are typically seen as black boxes, but with the PPU, their execution becomes deterministic and monitorable enough to be included in an ASIL-D system. By adhering to safety frameworks (such as ISO 26262 and the emerging ISO/PAS 8800 for AI), the PPU allows automotive engineers to leverage complex AI models for tasks like sensor interpretation or anomaly detection while still complying with safety standards. In conjunction with redundant sensing and cross-checking (e.g., comparing an AI-based output with a simpler physics-based calculation as a plausibility check), the PPU’s use can increase both the intelligence and safety of automotive systems.

In summary, the Infineon AURIX™ TC4x PPU is a pivotal addition to the microcontroller family’s architecture, marrying high performance parallel processing with the rigorous demands of real-time, safety-critical operation. It enables a new class of in-vehicle computations – from high-fidelity motor control to embedded deep learning – all within the envelope of an automotive-qualified, single-chip solution. This combination of capabilities makes the TC4x PPU a key enabler for the next generation of automotive and industrial innovations that require both computational muscle and uncompromising safety.

See Also

• TriCore Instruction Set Architecture
• Infineon AURIX TC4x
• emmtrix Code Vectorizer