67 citations · 84 across the 13 of their papers we have counts for
14 papers
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…
Harnessing Manycore Processors with Distributed Memory for Accelerated Training of Sparse and Recurrent Models
Jan Finkbeiner, Thomas Gmeinder, Mark Pupilli +2
Current AI training infrastructure is dominated by single instruction multiple data (SIMD) and systolic array architectures, such as Graphics Processing Units (GPUs) and Tensor Pro…
Online Transformers with Spiking Neurons for Fast Prosthetic Hand Control
Nathan Leroux, Jan Finkbeiner, Emre Neftci
Transformers are state-of-the-art networks for most sequence processing tasks. However, the self-attention mechanism often used in Transformers requires large time windows for each…
Training Spiking Neural Networks Using Lessons From Deep Learning
Jason K. Eshraghian, Max Ward, Emre Neftci +6
The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the futur…
Neural Sampling Machine with Stochastic Synapse allows Brain-like Learning and Inference
Sourav Dutta, Georgios Detorakis, Abhishek Khanna +3
Many real-world mission-critical applications require continual online learning from noisy data and real-time decision making with a defined confidence level. Probabilistic models…
On-Chip Error-triggered Learning of Multi-layer Memristive Spiking Neural Networks
Melika Payvand, Mohammed E. Fouda, Fadi Kurdahi +2
Recent breakthroughs in neuromorphic computing show that local forms of gradient descent learning are compatible with Spiking Neural Networks (SNNs) and synaptic plasticity. Althou…