Privacy Notice
You can find more information on the used cookies and how you can subsequently revoke your consent in our Privacy Policy.
Adaptive In-Memory Computing (AIM) Research Group
About
Memristive devices hold the promise to merge memory and processing (in-memory computing), thereby mitigating the computational burden imposed by the traditional von Neumann architecture, particularly for demanding machine learning and neuromorphic computing tasks. To transition from conventional (binary) storage devices to analog programmable computing primitives, a crucial enabler is the development of reliable and efficient resistance tuning mechanisms. To this end, we leverage machine learning techniques in conjunction with device characterization and physics-based models to identify optimal tuning schemes for specific applications.
Using the analog tunable memristive devices, this group further concentrates on leveraging brain-inspired adaptation and memory augmentation mechanisms to address the challenges of handling dynamic and unstructured environments. We aim to achieve neuromorphic machine intelligence exhibiting multi-time scale (adaptive in time) and multi-tasking (adaptive in tasks) capabilities, enabling the handling of more intricate and multi-modal applications.
Research Topics
- Analog Tunability of Memristive Devices: A Physics- and Machine Learning-Driven Approach
The steep non-linearity of memristive switching dynamics, coupled with inherent variations and asymmetry, presents significant challenges in achieving accurate and efficient control of analog conductance updates. By integrating electrical characterization, device physics, and machine learning techniques, we develop tailored models to analyze switching dynamics and optimize schemes for precise multi-level tunability. Effective implementation of analog tunability in memristive devices constitutes a crucial enabler for analog and adaptive in-memory computing. (Collaboration with PGI-7 and PGI-15) - Multi-Timescale Neuromorphic Computing & ML
This research is inspired by the biophysics of locally-activated and long-lasting dendritic spikes, triggered by synaptic inputs. This mechanism integrates two distinct timescales: rapid stimulus detection through synaptic transmission and slower integration of these events via long-lasting memory traces in the local membrane potential. We explore computing task improvements through hardware co-design inspired by this biological mechanism (Collaboration with Institute of Cognitive Science, Osnabrück). As a benchmark for this brain-inspired approach, we investigate the hardware implementation of specific machine learning algorithms, such as the Structured State-Space Sequence (S4/SSM) models, known for its effectiveness in capturing long-range dependencies in streaming data
(Collaboration with Dr. Sebastian Siegel and PGI-15). - Multi-Task Neuromorphic Processing & ML
Beyond context-independent feedforward propagation with synaptic weights, dendrites play a crucial role in facilitating contextual neuronal modulation within hierarchical sensory processing pathways, enabling multi-task representation learning. We investigate the computational benefits of hardware co-design inspired by this mechanism (Collaboration with Dendritic Learning Group, PGI-15). As a benchmark for this brain-inspired approach, we explore the hardware implementation of specific machine learning algorithms such as Model-Agnostic Meta-Learning (MAML) and Memory-Augmented Neural Networks (MANN), known for their promise in few-shot and continual learning scenarios.
Members
Publications
- State-Space Modeling and Tuning of Memristors for Neuromorphic Computing Applications, Proceedings of the 2023 International Conference on Neuromorphic Systems
- The Ouroboros of Memristors: Neural Networks Facilitating Memristor Programming, 2024 IEEE 6th International Conference on AI Circuits and Systems (AICAS)
- Neural Networks Facilitating Memristor Programming, Neuronics Conference 2024.
- Improved Memristor Control using Device Physics and Deep Reinforcement Learning, 2025 IEEE 7th International Conference on AI Circuits and Systems (AICAS)
- IMSSA: Deploying Modern State-Space Models on Memristive in-Memory Compute Hardware, 2025 IEEE International Symposium on Circuits and Systems (ISCAS)
Last Modified: 12.02.2025