By Dr. Sakyasingha Dasgupta.
The rapid development of artificial intelligence (AI) applications has created enormous demand for high-performance and energy-efficient computing systems. However, traditional homogeneous architectures based on Von Neumann processors face challenges in meeting the requirements of AI workloads, which often involve massive parallelism, large data volumes, and complex computations.
Heterogeneous computing architectures integrate different processing units with specialized capabilities and features and have emerged as promising solutions for AI applications. In our view, AI is driving the next era of software-defined heterogeneous computing, enabling better solutions for complex problems.
Read the full blog here
By Dr. Sakyasingha Dasgupta.
The rapid development of artificial intelligence (AI) applications has created enormous demand for high-performance and energy-efficient computing systems. However, traditional homogeneous architectures based on Von Neumann processors face challenges in meeting the requirements of AI workloads, which often involve massive parallelism, large data volumes, and complex computations.
Heterogeneous computing architectures integrate different processing units with specialized capabilities and features and have emerged as promising solutions for AI applications. In our view, AI is driving the next era of software-defined heterogeneous computing, enabling better solutions for complex problems.
Read the full blog here