Today, Arm unveiled new computing solutions to accelerate autonomous decision-making with safety capability across automotive and industrial applications. The new suite of IP includes the Arm Cortex-A78AE CPU, Arm Mali-G78AE GPU, and Arm Mali-C71AE ISP, engineered to work together in combination with supporting software, tools and system IP to enable silicon providers and OEMs to design for autonomous workloads. These products will be deployed in a range of applications, from enabling more intelligence and configurability in smart manufacturing to enhancing ADAS and digital cockpit applications in automotive.

"Autonomy has the potential to improve every aspect of our lives, but only if built on a safe and secure computing foundation," said Chet Babla, vice president, Automotive and IoT Line of Business at Arm. "As autonomous decision-making becomes more pervasive, Arm has designed a unique suite of technology that prioritizes safety while delivering highly scalable, power efficient compute to enable autonomous decision-making across new automotive and industrial opportunities."

Cortex-A78AE: High performance in safety critical applications

The new Arm Cortex-A78AE CPU is Arm's latest, highest performance safety capable CPU, offering the ability to run different, complex workloads for autonomous applications such as mobile robotics and driverless transportation. It delivers:

• A 30% performance uplift compared to its predecessor.
• Supports features to achieve the relevant automotive and industrial functional safety standards, ISO 26262 and IEC 61508 for applications up to ASIL D / SIL 3.
• New enhanced Split Lock functionality (Hybrid Mode) to offer maximum flexibility. Hybrid Mode is designed to specifically enable applications that target lower levels of ASIL requirements without compromising performance and allow the deployment of the same SoC compute architecture into different domain controllers.

Mali-G78AE: Redefining safety for embedded GPUs, with flexible partitioning

Mali is the number one shipping GPU worldwide, and the new Mali-G78AE is Arm's first GPU to be designed for safety, delivering rich user experiences and heterogenous compute to safety-critical autonomous applications. The new Mali-G78AE enables:

• A new approach to autonomous GPU workloads with Flexible Partitioning, with up to four fully independent partitions for workload separation for safety use cases.
• GPU resources can now be utilized for safety-enabled human machine interfaces or for the heterogenous compute needed in autonomous systems. For example, an infotainment system, an instrument cluster with ASIL B requirements and a driver monitoring system can now all run concurrently and independently with hardware separation within an automotive application.

Mali-C71AE: An evolution in ISP safety

Autonomous workloads need to be aware of their surroundings, often through cameras that must operate in a wide range of lighting conditions. To support a broad range of vision applications across automotive and industrial, the Mali-C71AE offers:

• The flexibility needed to support both human and machine vision applications such as production line monitoring and ADAS camera systems.
• Enhanced safety features, supports features to achieve ASIL B / SIL2 safety capability.
• Support for four real time cameras, or 16 buffered cameras, delivering a 1.2 giga pixel per second throughput.

Enabling the autonomous software ecosystem

As autonomous systems move towards more software-defined functionality, Arm is working to accelerate the development of software that will fully realize the benefits of these new technologies through initiatives such as Project Cassini, aimed at laying the foundation for the adoption of cloud native software paradigms across the entirety of edge computing. Arm is also working with multiple open source communities and specialist software vendors to widely enable the autonomous software ecosystem, adopting innovations from the established cloud native ecosystem, and collaboratively driving new development to support the features required for autonomous workloads.