The higher the level of autonomous driving, the more sensors and communication devices the vehicles need. This leads to an explosive increase in the amount of data to be processed. Because this electronic perception of the environment is a very time-critical matter, this data must also be processed without any delay. The complexity of acquisition and control tasks also requires the extensive use of artificial intelligence. In addition, the automotive industry is currently undergoing a phase of restructuring: Car manufacturers are increasingly converting their vehicles into platforms for providing digital services. This wealth of tasks can no longer be accomplished with conventional processor-based architectures, and even heterogeneous architectures with multiple different hardware accelerators often reach the limits of what is feasible - also because of their lack of flexibility in adapting to differently structured processing tasks. Adaptive platforms are needed.
This is one of the driving forces for Xilinx to develop a computing and signal processing architecture that is no longer subject to these limitations. More than a year ago, Xilinx CEO Victor Peng announced this architecture, now the first chips are available. Under the name "Adaptive Compute Acceleration Platform" (ACAP), the company is breaking new ground in the provisioning and configuration of chip-level computing resources, with the ACAP approach going far beyond the FPGAs and SoCs it has offered hitherto. ACAP is a highly integrated heterogeneous multi-core computing platform that can be flexibly modified at both the hardware and software levels (Fig.1).