Adaptive Baseband Pro cessing and Configurable Hardware for Wireless Communication

The world of information is literally at one’s fingertips, allowing access to previously unimaginable amounts of data, thanks to advances in wireless communication. The growing demand for high speed data has necessitated theuse of wider bandwidths, and wireless technologies such as Multiple-InputMultiple-Output (MIMO) have been adopted to increase spectral efficiency.These advanced communication technologies require sophisticated signal processing, often leading to higher power consumption and reduced battery life.Therefore, increasing energy efficiency of baseband hardware for MIMO signal processing has become extremely vital. High Quality of Service (QoS)requirements invariably lead to a larger number of computations and a higherpower dissipation. However, recognizing the dynamic nature of the wirelesscommunication medium in which only some channel scenarios require complexsignal processing, and that not all situations call for high data rates, allowsthe use of an adaptive channel aware signal processing strategy to provide adesired QoS. Information such as interference conditions, coherence bandwidthand Signal to Noise Ratio (SNR) can be used to reduce algorithmic computations in favorable channels. Hardware circuits which run these algorithmsneed flexibility and easy reconfigurability to switch between multiple designsfor different parameters. These parameters can be used to tune the operations of different components in a receiver based on feedback from the digitalbaseband. This dissertation focuses on the optimization of digital basebandcircuitry of receivers which use feedback to trade power and performance. Aco-optimization approach, where designs are optimized starting from the algorithmic stage through the hardware architectural stage to the final circuitimplementation is adopted to realize energy efficient digital baseband hardwarefor mobile 4G devices. These concepts are also extended to the next generation5G systems where the energy efficiency of the base station is improved.This work includes six papers that examine digital circuits in MIMO wireless receivers. Several key blocks in these receiver include analog circuits thathave residual non-linearities, leading to signal intermodulation and distortion.Paper-I introduces a digital technique to detect such non-linearities and calibrate analog circuits to improve signal quality. The concept of a digital nonlinearity tuning system developed in Paper-I is implemented and demonstratedin hardware. The performance of this implementation is tested with an analogchannel select filter, and results are presented in Paper-II. MIMO systems suchas the ones used in 4G, may employ QR Decomposition (QRD) processors tosimplify the implementation of tree search based signal detectors. However,the small form factor of the mobile device increases spatial correlation, whichis detrimental to signal multiplexing. Consequently, a QRD processor capableof handling high spatial correlation is presented in Paper-III. The algorithm and hardware implementation are optimized for carrier aggregation, which increases requirements on signal processing throughput, leading to higher powerdissipation. Paper-IV presents a method to perform channel-aware processingwith a simple interpolation strategy to adaptively reduce QRD computationcount. Channel properties such as coherence bandwidth and SNR are used toreduce multiplications by 40% to 80%. These concepts are extended to usetime domain correlation properties, and a full QRD processor for 4G systemsfabricated in 28 nm FD-SOI technology is presented in Paper-V. The designis implemented with a configurable architecture and measurements show thatcircuit tuning results in a highly energy efficient processor, requiring 0.2 nJ to1.3 nJ for each QRD. Finally, these adaptive channel-aware signal processingconcepts are examined in the scope of the next generation of communicationsystems. Massive MIMO systems increase spectral efficiency by using a largenumber of antennas at the base station. Consequently, the signal processingat the base station has a high computational count. Paper-VI presents a configurable detection scheme which reduces this complexity by using techniquessuch as selective user detection and interpolation based signal processing. Hardware is optimized for resource sharing, resulting in a highly reconfigurable andenergy efficient uplink signal detector

Efficient and Scalable Computing for Resource-Constrained Cyber-Physical Systems: A Layered Approach

With the evolution of computing and communication technology, cyber-physical systems such as self-driving cars, unmanned aerial vehicles, and mobile cognitive robots are achieving increasing levels of multifunctionality and miniaturization, enabling them to execute versatile tasks in a resource-constrained environment. Therefore, the computing systems that power these resource-constrained cyber-physical systems (RCCPSs) have to achieve high efficiency and scalability. First of all, given a fixed amount of onboard energy, these computing systems should not only be power-efficient but also exhibit sufficiently high performance to gracefully handle complex algorithms for learning-based perception and AI-driven decision-making. Meanwhile, scalability requires that the current computing system and its components can be extended both horizontally, with more resources, and vertically, with emerging advanced technology. To achieve efficient and scalable computing systems in RCCPSs, my research broadly investigates a set of techniques and solutions via a bottom-up layered approach. This layered approach leverages the characteristics of each system layer (e.g., the circuit, architecture, and operating system layers) and their interactions to discover and explore the optimal system tradeoffs among performance, efficiency, and scalability. At the circuit layer, we investigate the benefits of novel power delivery and management schemes enabled by integrated voltage regulators (IVRs). Then, between the circuit and microarchitecture/architecture layers, we present a voltage-stacked power delivery system that offers best-in-class power delivery efficiency for many-core systems. After this, using Graphics Processing Units (GPUs) as a case study, we develop a real-time resource scheduling framework at the architecture and operating system layers for heterogeneous computing platforms with guaranteed task deadlines. Finally, fast dynamic voltage and frequency scaling (DVFS) based power management across the circuit, architecture, and operating system layers is studied through a learning-based hierarchical power management strategy for multi-/many-core systems

Power Profiling Model for RISC-V Core

The reduction of power consumption is considered to be a critical factor for efficient computation of microprocessors. Therefore, it is necessary to implement a power management system that is aware of the computational load of the CPU cores. To enable such power management, this project aims to develop a power profiling model for the RISC-V core. TheSyDeKick verification environment was used to develop the power profiling models. Additionally, Python-controlled mixed mode simulations of C-programs compiled for A-Core were conducted to obtain needed data for the power profiling of the digital circuitry. The proposed methodology could employ a time-varying power consumption profiling for the A-Core RISC-V microprocessor core which depends on software, voltage, and clock frequency. The results of this project allow for the creation of parameterized power profiles for the A-Core, which can contribute to more efficient and sustainable computing

Design methodology for reliable and energy efficient self-tuned on-chip voltage regulators

The energy-efficiency needs in computing systems, ranging from high performance processors to low-power devices is steadily on the rise, resulting in increasing popularity of on-chip voltage regulators (VR). The high-frequency and high bandwidth on-chip voltage regulators such as Inductive voltage regulators (IVR) and Digital Low Dropout regulators (DLDO) significantly enhance the energy-efficiency of a SoC by reducing supply noise and enabling faster voltage transitions. However, IVRs and DLDOs need to cope with the higher variability that exists in the deep nanometer digital nodes since they are fabricated on the same die as the digital core affecting performance of both the VR and digital core. Moreover, in most modern SoCs where multiple power domains are preferred, each VR needs to be designed and optimized for a target load demand which significantly increases the design time and time to market for VR assisted SoCs. This thesis investigates a performance-based auto-tuning algorithm utilizing performance of digital core to tune VRs against variations and improve performance of both VR and the core. We further propose a fully synthesizable VR architecture and an auto-generation tool flow that can be used to design and optimize a VR for given target specifications and auto-generate a GDS layout. This would reduce the design time drastically. And finally, a flexible precision IVR architecture is also explored to further improve transient performance and tolerance to process variations. The proposed IVR and DLDO designs with an AES core and auto-tuning circuits are prototyped in two testchips in 130nm CMOS process and one test chip in 65nm CMOS process. The measurements demonstrate improved performance of IVR and AES core due to performance-based auto-tuning. Moreover, the synthesizable architectures of IVR and DLDO implemented using auto-generation tool flow showed competitive performance with state of art full custom designs with orders of magnitude reduction in design time. Additional improvement in transient performance of IVR is also observed due to the flexible precision feedback loop design.Ph.D