One of the reasons for the explosive growth of IoT is that embedded devices with networking capabilities and sensor interfaces are cheap enough to deploy them at a plethora of locations.
However, network bandwidth is limited. Not only that, but also, the latency of the network can be of seconds or minutes. By the time the sensor data is acquired by the centralized computers, its value for decision making could be lost. In other words, for the IoT solution to be effective, it should not only deliver meaningful data securely (and filter it as much as possible to avoid network congestion), it should also analyze it and act upon it at the origination point of the data. At the very edge of the network.
The concept of machine learning is not new. Attempts at systems emulating intelligent behavior, like expert systems, go as far back as the early 1980’s. And the very notion of modern Artificial Intelligence has a long history. The name itself was coined at a Dartmouth College conference (1956), but the idea of an “electronic brain” was born together with the development of modern computers. AI as an idea accompanies us from the dawn of human history.
Three latest development are pushing forward “Machine Learning”:
Powerful distributed processors
Cheap and high volume storage
High bandwidth interconnection to bring the data to the processors
The Stratix® 10 MX DRAM system-in-package (SiP) family combines a 1 GHz high-performance monolithic FPGA fabric, state-of-the-art Intel Embedded Multi-die Interconnect Bridge (EMIB) technology, and High Bandwidth Memory 2 (HBM2), all in a single package (SiP).