Enabling Edge-Intelligence in Resource-Constrained Autonomous Systems

The objective of this research is to shift Machine Learning algorithms from resource-extensive server/cloud to compute-limited edge nodes by designing energy-efficient ML systems. Multiple sub-areas of research in this domain are explored for the application of drone autonomous navigation. Our principal goal is to enable the UAV to autonomously navigate using Reinforcement Learning, without incurring any additional hardware or sensor cost. Most of the lightweight UAVs are limited in their resources such as compute capabilities and onboard energy source, and the conventional state-of-the-art ML algorithms cannot be directly implemented on them. This research addresses this issue by devising energy-efficient ML algorithms, modifying existing ML algorithms, designing energy-efficient ML accelerators, and leveraging the hardware-algorithm co-design. RL is notorious for being data-hungry and requires trials and error for it to converge. Hence it cannot be directly implemented on real drones until the issues of safety, data limitations, and reward generation is addressed. Instead of learning the task from scratch, just like humans, RL algorithms can benefit from prior knowledge which can help them converge to their goals in less time and consume less energy. Multiple drones can be collectively used to help each other by sharing their locally learned knowledge. Such distributive systems can help agents learn their respective local tasks faster but may become vulnerable to attacks in the presence of adversarial agents which needs to be addressed. Finally, the improvement in the energy efficiency of RL-based systems achieved from the algorithmic approaches is limited by the underlying hardware and computing architectures. Hence, these need to be redesigned in an application-specific way exploring and exploiting the nature of the most used ML operators This can be done by exploring new computing devices and considering the data reuse and dataflow of ML operators within the architectural design. This research discusses these issues by addressing them and presenting better alternatives. It is concluded that energy consumption at multiple levels of hierarchy needs to be addressed by exploring algorithmic, hardware-based, and algorithm-hardware co-design approaches.Ph.D

Thermal Aware Design Automation of the Electronic Control System for Autonomous Vehicles

The autonomous vehicle (AV) technology, due to its tremendous social and economical benefits, is transforming the entire world in the coming decades. However, significant technical challenges still need to be overcome until AVs can be safely, reliably, and massively deployed. Temperature plays a key role in the safety and reliability of an AV, not only because a vehicle is subjected to extreme operating temperatures but also because the increasing computations demand more powerful IC chips, which can lead to higher operating temperature and large thermal gradient. In particular, as the underpinning technology for AV, artificial intelligence (AI) requires substantially increased computation and memory resources, which have been growing exponentially through recent years and further exacerbated the thermal problems. High operating temperature and large thermal gradient can reduce the performance, degrade the reliability, and even cause an IC to fail catastrophically. We believe that dealing with thermal issues must be coupled closely in the design phase of the AVs’ electronic control system (ECS). To this end, first, we study how to map vehicle applications to ECS with heterogeneous architecture to satisfy peak temperature constraints and optimize latency and system-level reliability. We present a mathematical programming model to bound the peak temperature for the ECS. We also develop an approach based on the genetic algorithm to bound the peak temperature under varying execution time scenarios and optimize the system-level reliability of the ECS. We present several computationally efficient techniques for system-level mean-time-to-failure (MTTF) computation, which show several orders-of-magnitude speed-up over the state-of-the-art method. Second, we focus on studying the thermal impacts of AI techniques. Specifically, we study how the thermal impacts for the memory bit flipping can affect the prediction accuracy of a deep neural network (DNN). We develop a neuron-level analytical sensitivity estimation framework to quantify this impact and study its effectiveness with popular DNN architectures. Third, we study the problem of incorporating thermal impacts into mapping the parameters for DNN neurons to memory banks to improve prediction accuracy. Based on our developed sensitivity metric, we develop a bin-packing-based approach to map DNN neuron parameters to memory banks with different temperature profiles. We also study the problem of identifying the optimal temperature profiles for memory systems that can minimize the thermal impacts. We show that the thermal aware mapping of DNN neuron parameters on memory banks can significantly improve the prediction accuracy at a high-temperature range than the thermal ignorant for state-of-the-art DNNs