67 citations · 84 across the 13 of their papers we have counts for
9 papers · 1 filter
Optimal Gradient Checkpointing for Sparse and Recurrent Architectures using Off-Chip Memory
Wadjih Bencheikh, Jan Finkbeiner, Emre Neftci
Recurrent neural networks (RNNs) are valued for their computational efficiency and reduced memory requirements on tasks involving long sequence lengths but require high memory-proc…
Unsupervised Learning of Spatio-Temporal Patterns in Spiking Neuronal Networks
Florian Feiler, Emre Neftci, Younes Bouhadjar
The ability to predict future events or patterns based on previous experience is crucial for many applications such as traffic control, weather forecasting, or supply chain managem…
Gain Cell-Based Analog Content Addressable Memory for Dynamic Associative tasks in AI
Paul-Philipp Manea, Nathan Leroux, Emre Neftci +1
Analog Content Addressable Memories (aCAMs) have proven useful for associative in-memory computing applications like Decision Trees, Finite State Machines, and Hyper-dimensional Co…
On-Chip Learning via Transformer In-Context Learning
Jan Finkbeiner, Emre Neftci
Autoregressive decoder-only transformers have become key components for scalable sequence processing and generation models. However, the transformer's self-attention mechanism requ…
Analog In-Memory Computing Attention Mechanism for Fast and Energy-Efficient Large Language Models
Nathan Leroux, Paul-Philipp Manea, Chirag Sudarshan +4
Transformer networks, driven by self-attention, are central to Large Language Models. In generative Transformers, self-attention uses cache memory to store token projections, avoid…
SNNAX -- Spiking Neural Networks in JAX
Jamie Lohoff, Jan Finkbeiner, Emre Neftci
Spiking Neural Networks (SNNs) simulators are essential tools to prototype biologically inspired models and neuromorphic hardware architectures and predict their performance. For s…
Integration of Physics-Derived Memristor Models with Machine Learning Frameworks
Zhenming Yu, Stephan Menzel, John Paul Strachan +1
Simulation frameworks such MemTorch, DNN+NeuroSim, and aihwkit are commonly used to facilitate the end-to-end co-design of memristive machine learning (ML) accelerators. These simu…
The Ouroboros of Memristors: Neural Networks Facilitating Memristor Programming
Zhenming Yu, Ming-Jay Yang, Jan Finkbeiner +3
Memristive devices hold promise to improve the scale and efficiency of machine learning and neuromorphic hardware, thanks to their compact size, low power consumption, and the abil…
Efficient Event-Based Object Detection: A Hybrid Neural Network with Spatial and Temporal Attention
Soikat Hasan Ahmed, Jan Finkbeiner, Emre Neftci
Event cameras offer high temporal resolution and dynamic range with minimal motion blur, making them promising for robust object detection. While Spiking Neural Networks (SNNs) on…