Designing Reliable and Power-Efficient VLSI Circuits and Systems

Digital information management is the key enabler for the unparalleled rise in productivity and efficiency gains experienced by the world economies. Computing and information processing systems are important elements of the world’s digital infrastructure by providing ever-present and ever-increasing general purpose and data-driven processing and storage capabilities for both wired and mobile users. As such, they are also significant drivers of economic growth and social change. However, continued expansion of computing and information processing systems is now hindered by their unsustainable and rising power needs, with associated electrical energy costs and peak power draw requirements. Moreover governments, people, and corporations are becoming increasingly concerned about the environmental impact of these systems i.e., their carbon footprint. Separately from all this, with the increasing levels of variability in the characteristics of nanoscale CMOS devices and on-chip interconnects and continued uncertainty in the operating conditions of VLSI circuits, achieving power efficiency and high performance in computing and information processing systems under process, voltage, and temperature variations as well as interconnect wear-out and device aging has become a daunting, yet vital, task.

Keynote speech given at the 2011 International Symp. on Physical Design, Santa Barbara, CA — Robust Design of Power-Efficient VLSI Circuits

It is against the backdrop of rising power demands and energy costs as well as increased device- and circuit-level variability and aging effects that I present a number of best practices and methods for improving the power-performance efficiency of VLSI circuits and systems. The reviewed techniques range from dynamic power management to design of power-aware circuits, and from power/clock gating to leakage power minimization. A key issue to be addressed is how to deal with process and environment-induced variability of circuit parameters through statistical modeling and robust optimization and how to manage uncertainty about the workload and input data characteristics through observations and closed feedback loop control.