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.

While the notion of self-awareness has a long history in biology, psychology, medicine, engineering and (more recently) computing, we are seeing the emerging need for self-awareness in the context of complex Systems-on-Chip that must address the often conflicting requirements of performance, resiliency, energy, cost, etc. in the face of highly dynamic operational behaviors coupled with process, environment, and workload variabilities. Unlike traditional Systems-on-Chip (SoCs), self-aware SoCs must deploy an intelligent co-design of the control, communication, and computing infrastructure that interacts with the physical environment in real-time in order to modify the systems behavior so as to adaptively achieve desired objectives and Quality-of-Service (QoS). Self-aware SoCs require a combination of ubiquitous sensing and actuation, health-monitoring, and self-learning to enable the SoCs adaptation over time and space. This special session targets self-learning and self-awareness in two domains. The first one is a self-learning runtime reliability prediction approach by reusing Design-for-Test (DfT) infrastructure. The other one discusses real-time systems and applications to wireless communication, signal processing and control.

The quality of a communication link is commonly indicated by signal to noise/interference ratio and affected by noise and interference variance. To ensure a power efficient adaptive OFDM system's performance, the system needs to be tested against various channel conditions. Conventional algorithms and experiments assume that the noise and interference density stays constant over the OFDM frequency band but in reality the communication link often populated with both white noise and interference that results an uneven impact on OFDM spectrum. To create such channel condition, an up-conversion is commonly used. However, due to the non-linearity of the mixer, the up-converted interference is always distorted. The proposed method uses a modified higher-than-Nyquist-rate RF signal generation algorithm to minimize the distortion of generated interference within a pre-defined output bandwidth. Both concept validation and experimental measurement have been conducted to prove the effect of proposed method.

Modern radio front ends are required to operate over diverse channel conditions requiring the incorporation of significant performance overheads into their design. This results in significantly higher power consumption over most of the operational period since the worst case channels are not statistically prevalent. Adaptive systems solve this problem by adapting the performance and power consumption depending on channel conditions. In this paper, it is demonstrated that depending upon the throughput requirements of the system multiple low-power adaptation modes can be designed. These modes ensure either highest throughput operation or lowest energy-per-bit operation. The operation of these modes is demonstrated in simulation using multiple-input-multiple-output (MIMO) receiver and transmitter front ends. Subsequently, the concepts are demonstrated in hardware for both MIMO and single-input-single-output front ends.

Prior research has established that dynamically trading-off the performance of the RF front-end for reduced power consumption across changing channel conditions, using a feedback control system that modulates circuit and algorithmic level "tuning knobs" in real-time, leads to significant power savings. It is also known that the optimal power control strategy depends on the process conditions corresponding to the RF devices concerned. This complicates the problem of designing the feedback control system that guarantees the best control strategy for minimizing power consumption across all channel conditions and process corners. Since this problem is largely intractable due to the complexity of simulation across all channel conditions and process corners, we propose a self-learning strategy for adaptive MIMO-RF systems. In this approach, RF devices learn their own performance vs. power consumption vs. tuning knob relationships "on-the-fly" and formulate the optimum reconfiguration strategy using neural-network based learning techniques during real-time operation. The methodology is demonstrated for a MIMO-RF receiver front-end and is supported by hardware validation leading to 2.5X power savings in minimal learning time.

Modern MIMO RF transceiver systems are designed to operate reliably under diverse channel conditions leading to incorporation of significant performance margins in RF transceiver systems. In general, across dynamically varying channel conditions, the fidelity of the RF front end devices can be traded-off against power consumption without compromising system-level BER limits. In this work such a real-time performance vs. power consumption modulation of RF front-end devices in MIMO systems is demonstrated. Through a multi-dimensional optimization technique, power-optimal configuration of the front-end for varying channel conditions are created. Additionally multiple low-power operating modes for the MIMO system are proposed depending on the performance metric (data rate or energy-per-bit) that need to be optimized for different applications.

A low cost methodology for simultaneous testing and tuning of multiple chains of MIMO-OFDM wireless transmitter for system-level specifications is presented. Bandwidth-partitioned test stimuli enable the determination of the behavioral characteristics of the different chains of the RF transmitter using a one-time data acquisition. The determined behavioral characteristics of the transmitters are then correlated to system-level specifications in the simulation environment. Using the test setup, a power conscious system-level tuning approach for yield improvement is developed for tuning of parametric deviations. A yield improvement of 20% is obtained using the proposed methodology. Finally, an adaptive tuning approach is presented for those devices that face increased reliability risks/power-budget violations due to the excessive power consumption caused by post-manufacturing tuning. The tuning methodology achieves new performance metrics for these devices that attempt to maximize the conditions under which the device operates. Significant improvement in yield is obtained using the adaptive tuning methodology. Preliminary hardware validation of the proposed methodology using off the shelf components is performed.

Testing of adaptive analog/RF systems is challenging as any test procedure must ensure that the system adapts correctly to external perturbations (process, workload) without incurring the excessive test time associated with iterative tuning procedures. This problem is made worse by the increased number of adaptation settings (“tuning knob values”) and the requirement of measuring specifications at all of these settings. In this paper, a new test technique is proposed that allows the closed loop performance of the adaptation procedure to be predicted from a set of open-loop tests. The optimal knob settings where the system should be tested in open-loop are found using a gradient based search algorithm and optimized test signals are generated such that the error in performance prediction across different tuning knob settings is minimized. The results of these tests are then mapped to the performance of the adaptive system which is validated implicitly without incurring large testing and tuning costs. Simulation results and hardware measurement results prove the validity of the proposed technique.

Earlier research has demonstrated that the power in a RF front-end can be traded off for performance in real-time to operate at the threshold of acceptable operation using a lookup table driven controller. Such a controller needs careful calibration and suffers from modeling inaccuracies which must be guardbanded during real-time operation resulting in "less than optimal" power consumption. In this work we propose a real time fuzzy noise-distortion control algorithm for low-power adaptation of the RF front-end to continuously changing channel conditions. As opposed to prior techniques, the proposed control algorithm handles adaptation to channel attenuation and fading as well as in-band and out- of- band interference, taking advantage of signal and interferer estimation techniques that are already incorporated in modern transceivers. In the proposed technique, the RF front-end controller automatically finds an optimal power-performance trade-off point in real-time across a large gamut of channel conditions. It is seen that about 23% savings in power consumption can be obtained over RF systems that are not capable of real-time adaptation.