11 citations · 11 across the 3 of their papers we have counts for
6 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…
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…
Efficient Data Selection Methods for the Development of Machine Learned Potentials
Jan Finkbeiner, Samuel Tovey, Christian Holm
We present an investigation into data selection methods for the efficient sampling of configuration space as applied to the development of inter-atomic potentials for scale bridgin…
Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometry Constrained Keypoints in Real-Time
Nils Gählert, Jun-Jun Wan, Nicolas Jourdan +3
In this paper we propose a novel 3D single-shot object detection method for detecting vehicles in monocular RGB images. Our approach lifts 2D detections to 3D space by predicting a…