Charlotte Frenkel
Papers
3
Total Citations
144
H-Index
2
About
Charlotte Frenkel is a leading researcher at the intersection of neuromorphic computing and edge intelligence, specializing in spiking neural networks (SNNs) and energy-efficient hardware design. Her major contributions center on enabling on-chip learning for autonomous systems, particularly through her pioneering work on the ReckOn processor—a 28nm, sub-mm² spiking recurrent neural network accelerator that supports task-agnostic, always-on adaptation over second-long timescales. This work, cited over 130 times, addresses the critical challenge of data distribution shifts in real-world deployments, paving the way for robust, lifelong learning at the edge. Frenkel has also advanced adaptive robotic control, demonstrating how SNNs on digital accelerators can reduce computational footprints while maintaining performance in resource-constrained scenarios. Her research bridges neuroscience-inspired models with practical hardware implementations, earning recognition for its impact on low-power AI. With a focus on scalability and real-time adaptation, Frenkel’s work is shaping the future of autonomous systems, from robotics to IoT devices, making her a key figure in neuromorphic engineering.
Research Focus
Key Achievements
Top Papers
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