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
“Field programmable gate arrays (FPGAs) are increasingly complex system on chips (SoCs) that include not just programmable logic gates and random access memory (RAM) but also analog-to-digital converters (ADCs); digital-to-analog converters (DACs); and programmable analog features and signal-conditioning circuits that enable high-performance digital computations in servers, network-attached storage (NAS), enterprise switches, oscilloscopes, network analyzers, test equipment and software-defined radios.”
Modern FPGA devices are quite complex machines. They include support for several type of I/Os at different voltages (LVCMOS, LVDS, SSTL, etc). Also, the FPGA core usually works at low voltages of around 1.0V, but at quite high currents of several amperes. Additionally, power sequencing requirements must be met.