Data-driven experiences are rich, immersive and immediate. Think pizza delivery by drone, video cameras that can record traffic accidents at an intersection, freight trucks that can identify a potential system failure. These kinds of fast-acting activities need lots of data -- quickly. So they can't sustain latency as data travels to and from the cloud. That to-and-fro takes too long.

Data-driven experiences are rich, immersive and immediate. Think pizza delivery by drone, video cameras that can record traffic accidents at an intersection, freight trucks that can identify a potential system failure. These kinds of fast-acting activities need lots of data -- quickly. So they can't sustain latency as data travels to and from the cloud. That to-and-fro takes too long; instead, many of these data-intensive processes must remain localized and processed at the edge and on or near a hardware device.

The rapid adoption of artificial intelligence (AI) for practical business applications has introduced a number of uncertainties and risk factors across virtually every industry, but one fact is certain: in today's AI market, hardware is the key to solving many of the sector's key challenges, and chipsets are at the heart of that hardware solution. Given the widespread applicability of AI, it is almost certain that every chip in the future will have some sort of AI engine embedded. The engine could take a wide variety of forms, ranging from a simple AI library running on a CPU to more sophisticated custom hardware. The potential for AI is best fulfilled when the chipsets are optimized to provide the appropriate amount of compute capacity at the right power budget for specific AI applications, a trend that is leading to increasing specialization and diversification in AI-optimized chipsets. During the past 2 years, the deep learning chipset market has experienced a dramatic period of evolution, led by NVIDIA and Intel.