Artificial intelligence is bringing new demands to processors. The algorithmic data crunching is different from earlier models of processing data highlighted by benchmarks like LINPACK. It is also changing computing architectures by de-emphasizing the CPU and harnessing the faster computing power of coprocessors. The CPU is just a facilitator, and a lot of deep-learning is done on accelerator chips like GPUs, FPGAs and Google's Tensor processing unit. Major hardware companies like IBM, Intel, Nvidia and AMD are embracing the change in architecture and tuning hardware that encourage the creation of artificial neural nets, as envisioned by researchers in 1960s.

Speed to insight is going to emerge as the key competitive differentiator for businesses, as they start stepping into the era of compute-and-speed-hungry artificial intelligence(AI), and deep learning workloads. IBM, recently announced a new line of accelerated IBM Power Systems Servers, keeping this new requirement of businesses in mind. The systems are built on its new POWER9 processor, which reduces the training times of deep learning frameworks significantly from days to hours and allows building more accurate AI applications in considerably less time. "The era of AI demands a tremendous amount of processing power at unprecedented speed," said Monica Aggarwal, Vice President, IBM India Systems Development Lab. "To meet the demands of the cognitive workload, businesses need to change everything right from the start- the algorithms, the software, and the hardware as well. POWER9 systems bring an integrated AI platform designed to accelerate machine learning and deep learning with both software and hardware that are optimized to work together."

The Power9 chips in the systems hitting the market now don't rev up to the top speeds provided by Intel's Xeon Scalable Processor line, but they offer blazing throughput aimed to give them an edge in machine learning and accelerated database applications.