Self learning analog/mixed-signal/RF systems: Dynamic adaptation to workload and environmental uncertainties

Abstract

Real-time systems for wireless communication, digital signal processing and control experience a wide gamut of operating conditions (signal/channel noise, workload demand, perturbed process conditions). As device bandwidths expand, it becomes increasingly expensive, from a power consumption and reliability perspective, to operate such real-time systems for worst-case (static) performance requirements. In contrast, it is attractive to design algorithms, architectures and circuits that are power-performance tunable and can adapt dynamically, via self-learning techniques, to the requirements of system-level applications for extended battery usage and device lifetime. Such future systems will feed application level demands to the underlying algorithm-architecture-circuit design fabric through built-in sense-and-control infrastructure (hardware, software). The sense functions assess instantaneous application level demands (e.g. throughput, signal integrity) as well as the performances of the individual hardware components as determined by manufacturing process conditions. The control functions actuate algorithm-through-circuit level tuning knobs that continuously trade off performance vs. power of the individual software and hardware modules in such a way as to deliver the end-to-end desired application level Quality of Service (QoS), while minimizing energy/power consumption. Application to wireless communications systems, digital signal processing and control algorithms is discussed.