Another aspect to remind in the product description is AMD’s SenseMI technology: a package of five related “senses” that are relying on learning algorithms and/or the command-and-control functionality of the Infinity Fabric, in order to empower AMD Ryzen processors with machine intelligence.

Pure Power

Thanks to the integrated network of smart sensors that are driving Precision Boost, processor power consumption can be carefully adjusted with any given workload. The telemetry data from the Power Power optimization loop does allow each Ryzen processor to inspect the characteristics of its own silicon, in order to extract individualized power management.

Precision Boost

By using current/temperature/load information fed by the Infinity Fabric, Precision Boost is modulating an AMD Ryzen processor in 25Mhz increments; this type of granular clock speed control is offering the Ryzen processor a greater operational freedom in order to reach ideal frequency targets and at the same time allows for finer dithering at that ideal target.

Extended Frequency Range or XFR

XFR is kicking in when high-performance cooling systems are installed on the AMD Ryzen processors and lifts the maximum Precision Boost frequency beyond the ordinary limits. This is achieved by reading and forecasting AMD Ryzen processor’s distance to TJMax, then converting the available headroom into extra frequency. For non-X SKUs (such as the Ryzen 7 1700), XFR will add 50MHz extra to the operating frequencies, while X SKUs (such as the Ryzen 7 1800X, Ryzen 7 1700X) will add 100MHz extra to the stock operating frequency when the said conditions are met.

Neural Net Prediction

Every AMD Ryzen processor holds a true artificial intelligence inside which harnesses a neural network for learning in real-time the applications’ behavior and speculate on its next moves. Thanks to this feature, the AI readies vital CPU instructions in advance for tackling a new workload.

Smart Prefetch

Thanks to the integrated sophisticated learning algorithms, internal patterns and behaviors of applications are understood, so they can anticipate what data is needed for fast execution in the future. Data is fed into local cache, so it is ready for immediate use.