Power Reduction of Digital Signal Processing Systems using Subthreshold Operation

Abstract

Over the past couple decades, the capabilities of battery-powered electronics has expanded dramatically. What started out as large bulky 2-way radios, wristwatches, and simple pacemakers, has evolved into pocket sized smart-phones, digital cameras, person digital assistants, and implantable biomedical chips that can restore hearing and prevent heart attacks. With this increase in complexity comes an increase in the amount of processing, which runs on a limited energy source such as a battery or scavenged energy. It is therefore desirable to make the hardware as energy efficient as possible. Many battery-powered systems require digital signal processing, which often makes up a large portion of the total energy consumption. The digital signal processing of a battery-powered system is therefore a good target for power reduction techniques. One method of reducing the power consumption of digital signal processing is to operate the circuit in the subthreshold region, where the supply voltage is lower than the threshold voltage of the transistors. Subthreshold operation greatly reduces the power and energy consumption, but also decreases the maximum operating frequency. Many digital signal processing applications have real-time throughput requirements, so various architectural level techniques, such as pipelining and parallelism, must be used in order to achieve the required performance.

This thesis investigates the use of parallelization and subthreshold operation to lower the power consumption of digital signal processing applications, while still meeting throughput requirements. Using an off the shelf fast fourier transform architecture, it will be shown that through parallelization and subthreshold operation, a 70% reduction in power consumption can be achieved, all while matching the performance of a nominal voltage single core architecture. Even better results can be obtained when an architecture is specifically designed for subthreshold operation. A novel Discrete Wavelet Transform architecture is presented that is designed to eliminate the need for memory banks, and a power reduction of 26x is achieved compared to a reference nominal voltage architecture that uses memory banks. Issues such as serial to parallel data distribution, dynamic throughput scaling, and memory usage are also explored in this thesis. Finally, voltage scaling greatly increases the design space, so power and timing analysis can be very slow due long SPICE simulation times. A simulation framework is presented that can characterize subthreshold circuits accurately using only fast gate level design automation tools.