Artificial intelligence will soon change how we conduct our daily lives. Are companies prepared to capture value from the oncoming wave of innovation? Yes, they have a fine MRI machine and powerful software to generate the images. But that's where the machines bog down. The radiologist has to find and read the patient's file, examine the images, and make a determination. What if artificial intelligence (AI) could jump-start that process by enabling real-time and more accurate diagnoses or guidance, beyond what human eyes can see?
The growth of artificial intelligence (AI) demands that semiconductor companies re-architect their system on chip (SoC) designs to provide more scalable levels of performance, flexibility, efficiency, and integration. From the edge to data centers, AI applications require a rethink of memory structures, the numbers and types of heterogeneous processors and hardware accelerators, and careful consideration of how the dataflow is enabled and managed between the various high-performance IP blocks. This paper will define AI, describe its applications, the problems it presents, and how designers can address those problems through new and holistic approaches to SoC and network on chip (NoC) design. It also describes challenges implementing AI functionality in automotive SoCs with ISO 26262 functional safety requirements.
Artificial intelligence is becoming an integral feature of most distributed computing architectures. As such, AI hardware accelerators have become a principal competitive battlefront in high tech, with semiconductor manufacturers such as NVIDIA, AMD, and Intel at the forefront. In recent months, vendors of AI hardware acceleration chips have stepped up their competitive battles. One of the most recent milestones was Intel's release of its new AI-optimized Ponte Vecchio generation of graphical processing units (GPUs), which is the first of several products from a larger Xe family of GPUs that will also accelerate gaming and high-performance computing workloads. In AI hardware acceleration, NVIDIA has been the chip vendor to beat, owing to its substantial market lead in GPUs and its continued enhancements in the chips' performance, cost, efficiency, and other features.