3 research outputs found
Power-compute co-design for robust pervasive IoT applications
PhD ThesisThe modern development of internet of things (IoT) requires the IoT devices to be more
compact and energy autonomous. Many of them require to be able to operate with
unstable and low power supplies that come from various energy sources such as energy
harvesters. This creates a challenge for building IoT devices that need to be robust to
energy variations.
In this research we propose methods for improving energy characteristics of IoT
devices from the perspective of two main challenges: (i) improving the efficiency
and stability of power regulators, and (ii) enhancing the energy robustness of the IoT
devices. The existing design methods do not consider these two aspects holistically. One
important feature of our approach is holistic use of event-based, temporal representation
of data, which involves using asynchronous techniques and duty-cycle-based encoding.
For power regulation we use switched-capacitor converters (SCC) because they offer
compactness and ease of on-chip implementation. In this research we adapt the existing
methods and develop new techniques for SCC design based on asynchronous circuits.
This allows us to improve their performance and stability. We also investigate the
methods of parasitic charge redistribution, and apply them to self-oscillating SCC,
improving their performance. The key contribution within (i) is development of the
methods of SCC design with improved characteristics.
The majority of novel IoT systems are shifting towards the “AI at the edge” vision,
for example, involving neural networks (NN). We consider a perceptron-based neural
network as a typical IoT computing device. In our research we propose a novel
NN design approach using the principle of pulse-width modulation (PWM). PWMencoded
signals represent information with their duty cycle values which may be made
independent of the voltages and frequencies of the carrier signals. As a result, the device
is more robust to voltage variations, and, thus, the power regulation can be simplified.
This is the second major contribution addressing challenge (ii).
The advantages of the proposed methods are validated with simulations in the
Cadence environment. The simulations demonstrate the operation of the designed
power regulators, and the improvements of their efficiency. The simulations also
demonstrate the principle of operation of the PWM-based perceptron and prove its
power and frequency elasticity.
The thesis gives future research directions into a deeper study of the holistic co-design
of a variation-robust power-compute paradigm and its impact on developing future IoT
applications