11 citations · 22 across the 13 of their papers we have counts for
14 papers
Fast and Flexible Spiking Network Simulations on Massively Distributed On-Chip Memory Accelerators
Catherine Mia Schöfmann, Jan Finkbeiner, Jan Vogelsang +1
Studying brain function and dysfunction in spiking networks with realistic synaptic densities pushes simulation technology beyond its current limits. It requires simulations substa…
GRINQH: Graded Input-based Quantization Hierarchy for Efficient LLM Generation
Jette Oberländer, Jan Finkbeiner, Catherine M. Schöfmann +1
Autoregressive decoding with LLMs is primarily bottlenecked by GPU memory bandwidth, especially in edge-computing settings. While quantization is essential for mitigating this bott…
Learning to Remember, Learn, and Forget in Attention-Based Models
Djohan Bonnet, Jamie Lohoff, Jan Finkbeiner +2
In-Context Learning (ICL) in transformers acts as an online associative memory and is believed to underpin their high performance on complex sequence processing tasks. However, in…
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…
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…
Generating Minimal Training Sets for Machine Learned Potentials
Jan Finkbeiner, Samuel Tovey, Christian Holm
This letter presents a novel approach for identifying uncorrelated atomic configurations from extensive data sets with a non-standard neural network workflow known as random networ…
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…
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…