Clare Thiem

United States Air Force Research Laboratory

Papers

Total Citations

H-Index

About

Clare Thiem’s research lies at the intersection of hardware-accelerated machine learning and bio-inspired computing, with a focus on creating efficient, low-power artificial intelligence for resource-constrained platforms. Her most cited work, “Hardware-based artificial neural networks for size, weight, and power constrained platforms” (2014, 2 citations), demonstrates a pioneering approach using a fully parallel, silicon-based neural network (CM1K) built on zero instruction set computer (ZISC) technology. This study successfully applied the hardware for real-time change detection and object identification in video data, highlighting fundamental pattern recognition capabilities even with reduced neuron counts—a critical advancement for embedded systems. More recently, Thiem’s 2023 paper, “Integrating complex valued hyperdimensional computing with modular artificial neural networks” (2 citations), tackles the challenge of “on the fly” learning, a capability biological systems excel at but deep neural networks struggle to replicate. By combining complex-valued hyperdimensional computing with modular neural architectures, she proposes a framework that mimics organisms’ multi-sensory processing, aiming to bridge the gap between traditional deep learning and general intelligence. Though her citation counts are modest, Thiem’s work is notable for its forward-looking, interdisciplinary approach, offering tangible solutions for deploying AI in size, weight, and power-constrained environments—a growing need in robotics, edge computing, and autonomous systems.