Rapid Prototyping Methodology of Lightweight Electronic Drivers for Smart Home Appliances

Many researches have been conducted in smart home topic. Mostly, they discussed on the specific aspect of application. On the other side, many applications still can be explored and attached into the system. Several main challenges in designing the application devices are system complexity, reliability, user friendliness, portability, and low power consumption. Thus, design of electronic driver is one of the key elements for overcoming these challenges. Moreover, the drivers have to comply the rules of smart home system, data protocol, and application purpose. Hence, we propose a rapid prototyping methodology on designing lightweight electronic drivers for smart home appliances. This methodology consists of three main aspects, namely smart home system understanding, circuitry concept, and programming concept. By using this method, functional and lightweight drivers can be achieved quickly without major changes and modifications in home electrical system. They can be remotely controlled and monitored anytime and from anywhere. For prototyping, we design several drivers to represent common electronic and mechanical based applications. Experimental results prove that the proposed design methodology can achieve the research target

Incentivizing Flexibility and Cooperation in Computer Systems Using Feedback Mechanisms

Many computer systems and applications, from small embedded systems to large datacenters have deployment requirements. Meeting these requirements in a dynamic environment is challenging and requires flexibility from the application and the system. Flexibility is the ability to trade-off the value of one measure space by adjusting the value of another measure space. For example, both DNN and approximate computing applications can reduce their runtime latency by sacrificing their output accuracy. However, managing this flexibility is difficult. Prior approaches do not incentivize flexibility and cooperation. In the single stakeholder scenario where applications come from one stakeholder, they do not cooperate with the application and system knobs which makes the deployment inefficient in terms of energy, output accuracy, and performance. In the multistakeholder scenario, they do not incentivize the flexibility of applications which make flexible application produce higher output error. Our first contribution is GRAPE and MERLOT, a hardware feedback mechanism to meet latency requirements while reducing energy usage. GRAPE is a hardware control system for GPU that provides a soft guarantee to meet the performance requirements. Meanwhile, MERLOT is a real-time hardware scheduler that provides a hard real-time guarantee. Our second contribution is ALERT, runtime management for Deep Neural Networks that decrease output error or energy usage while meeting latency requirements. ALERT achieves cooperation between application and system by coordinating the flexibility offered from both. ALERT uses a probabilistic feedback mechanism that predicts the energy, performance, and output accuracy of the applications during the runtime. The third contribution is SIM, runtime management that incentivizes the flexibility of applications in the multistakeholder scenario. Prior approaches inadvertently disincentivize flexibility by forcing flexible applications to adapt to meet their deployment requirement, thus encouraging greedy behavior where every stakeholder deploys inflexible approaches that consume as many resources as possible. SIM instead only enforces the adaptation for the application that holds the most resources. In each iteration, on behalf of the applications, SIM would make an application to a configuration that minimizes their output error such that the resource usage is either less than the application that holds most resources or higher as long as there are enough slack resources. SIM incentivize the deployment of flexible application by giving opportunity for all of the applications to fight for the slack resources by being flexible