High-Performance and Reliable Intermittent Computation
Abstract
An energy harvesting system (EHS) provides the intriguing possibility of battery-less computing and enables various applications such as wearable, industrial or environmental sensors, and implantable medical devices. The biggest challenge of EHS is the instability of energy sources (e.g., Wi-Fi, solar, thermal energy, etc.) which causes unpredictable and frequent power outages. To address the challenge, existing works introduce software-based and hardware-based power failure recovery solutions that ensure program correctness across a power outage. However, they cause a significant performance overhead without providing the high quality of service in reality, and suffer from a reliability issue. In this dissertation, we address the limitations of recovery solutions across the system stack, from the compiler-directed approach and run-time systems to hardware mechanisms, and demonstrate the effectiveness of the approaches using real EHS platforms and simulators. We first present software-based recovery solutions by leveraging compiler support. We develop a compiler-directed solution built upon commodity EHS platform that can achieve 3X speedup compared to the software-based state-of-the-art solution. We also introduce a compiler optimization technique that can cooperate with run-time systems and hardware support, achieving 8X speedup compared to the software-based solution. We then present hardware-based recovery solutions by leveraging compiler and hardware support. We develop an architecture/- compiler co-design solution that re-purposes existing hardware components in a core for power failure speculative execution, a new speculation paradigm, and leverages a novel compiler analysis for correct power failure recovery. Our result highlights 2 ∼ 3x performance improvement compared to the hardware-based state-of-the-art solution without requiring hardware modification. Next, we present a new cache design for EHS that can achieve cost-effective, high-performance intermittent computing. According to experimental results, the new cache design outperforms the state-of-the-art cache scheme by 4X and reduces the hardware cost by 90%. Finally, we present an operating system (OS)-driven solution to address a reliability problem on EHS devices while all existing works are vulnerable, causing the wrong recovery across power failure. Our experiments demonstrate that the solution causes less than 1% run-time overhead and successfully addresses the reliability problem without compromising correct power failure recovery.
Degree
Ph.D.
Advisors
Jung, Purdue University.
Subject Area
Energy
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