Smart energy management is indispensable in modern radios. This book describes and applies an energy-driven design strategy to the design of an energy-efficient, highly scalable, pulsed UWB receiver, suitable for low data rate communication and sub-cm ranging.

Smart energy management, both at design time and at run time, is indispensable in modern radios. It requires a careful trade-off between the system's performance, and its power consumption. Moreover, the design has to be dynamically reconfigurable to optimally balance these parameters at run time, depending on the current operating conditions.

Energy Scalable Radio Design describes and applies an energy-driven design strategy to the design of an energy-efficient, highly scalable, pulsed UWB receiver, suitable for low data rate communication and sub-cm ranging. This book meticulously covers the different design steps and the adopted optimizations: System level air interface selection, architectural/algorithmic design space exploration, algorithmic refinement (acquisition, synchronization and ranging algorithms) and circuit level (RTL) implementation based on the FLEXmodule-concept. Measurement results demonstrate the effectiveness and necessity of the energy-driven design strategy.

Covers energy-driven design strategy, including novel "FLEXmodule" implementation concept Explains need for run-time reconfigurability and how to introduce it at design time Describes complete system-to-(digital)circuit design of an IR-UWB based radio Discusses algorithms and strategies for sub-cm ranging and fast data acquisition All results are validated on actual silicon implemented designs

Autorentext

Laura Isabel Galindez Olascoaga obtained her M.Sc. degree in Systems and Control from the Technical University of Eindhoven, The Netherlands, in 2015 and her Ph.D. degree in Electrical Engineering from KU Leuven, Belgium, in 2020. During the winter of 2018, she was a visiting scholar at the Statistical and Relational Artificial Intelligence (StarAI) lab of UCLA. She is currently a postdoctoral researcher at the Berkeley Wireless Research Center (BWRC) in UC Berkeley, where she investigates how to exploit the paradigm of Hyperdimensional Computing in applications that require intelligent feedback loops. Wannes Meert received his degrees of Master of Electrotechnical Engineering, Micro-electronics (2005), Master of Artificial Intelligence (2006) and Ph.D. in Computer Science (2011) from KU Leuven. He is a research manager in the DTAI section at KU Leuven. His work is focused on applying machine learning, artificial intelligence and anomaly detection technology to industrial application domains. Marian Verhelst is an associate professor at the MICAS laboratories of the EE Department of KU Leuven. Her research focuses on embedded machine learning, hardware accelerators, HW-algorithm co-design and low-power edge processing. Before that, she received a PhD from KU Leuven in 2008, was a visiting scholar at the BWRC of UC Berkeley in the summer of 2005, and worked as a research scientist at Intel Labs, Hillsboro OR from 2008 till 2011. Marian is a member of the DATE and ISSCC executive committees, is TPC co-chair of AICAS2020 and tinyML2020, and TPC member DATE and ESSCIRC. Marian is an SSCS Distinguished Lecturer, was a member of the Young Academy of Belgium, an associate editor for TVLSI, TCAS-II and JSSC and a member of the STEM advisory committee to the Flemish Government. Marian currently holds a prestigious ERC Starting Grant from the European Union and was the laureate of the Royal Academy of Belgium in 2016.

Inhalt
and Motivation.- Adaptation of Classical Design Flow for Energy-Driven System-to-Circuit Design.- System Level Specifications and Design.- Algorithmic/Architectural Design Space Exploration.- Algorithmic/Architectural Level Refinement.- Digital RT Level Design: Flexibility to Save Energy.- Chip and System Measurements.- Conclusions.