11 citations · 22 across the 13 of their papers we have counts for
6 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…
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…
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…