18 citations · 39 across the 11 of their papers we have counts for
11 papers
The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing
Stefan Scholze, Johannes Partzsch, Sebastian Höppner +27
In deep learning, efficiency gets more and more important to compensate for the ongoing growth in model sizes and applications. Neuromorphic hardware has long been advocated as an…
Event-based Neural Decoding for Neuroprosthetic Motor Control
Khaleelulla Khan Nazeer, Sirine Arfa, Matthias Jobst +2
A substantial number of patients experience diminished mobility due to disabilities, diseases, or accidents. Although modern prostheses, powered by deep neural networks, hold the p…
Heterogeneous computing platform for real-time robotics
Jakub Fil, Yulia Sandamirskaya, Hector Gonzalez +18
After Industry 4.0 has embraced tight integration between machinery (OT), software (IT), and the Internet, creating a web of sensors, data, and algorithms in service of efficient a…
STREAM: A Universal State-Space Model for Sparse Geometric Data
Mark Schöne, Yash Bhisikar, Karan Bania +4
Handling sparse and unstructured geometric data, such as point clouds or event-based vision, is a pressing challenge in the field of machine vision. Recently, sequence models such…
LAYUP: Asynchronous decentralized gradient descent with LAYer-wise UPdates
Cabrel Teguemne Fokam, Marcel Nieveler, Lukas König +3
The increasing size of deep learning models has made distributed training across multiple devices essential. Synchronous, centralized methods incur large communication and synchron…
Weight Sparsity Complements Activity Sparsity in Neuromorphic Language Models
Rishav Mukherji, Mark Schöne, Khaleelulla Khan Nazeer +3
Activity and parameter sparsity are two standard methods of making neural networks computationally more efficient. Event-based architectures such as spiking neural networks (SNNs)…
Language Modeling on a SpiNNaker 2 Neuromorphic Chip
Khaleelulla Khan Nazeer, Mark Schöne, Rishav Mukherji +4
As large language models continue to scale in size rapidly, so too does the computational power required to run them. Event-based networks on neuromorphic devices offer a potential…
SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning
Hector A. Gonzalez, Jiaxin Huang, Florian Kelber +11
The joint progress of artificial neural networks (ANNs) and domain specific hardware accelerators such as GPUs and TPUs took over many domains of machine learning research. This de…
Activity Sparsity Complements Weight Sparsity for Efficient RNN Inference
Rishav Mukherji, Mark Schöne, Khaleelulla Khan Nazeer +2
Artificial neural networks open up unprecedented machine learning capabilities at the cost of ever growing computational requirements. Sparsifying the parameters, often achieved th…
Block-local learning with probabilistic latent representations
David Kappel, Khaleelulla Khan Nazeer, Cabrel Teguemne Fokam +2
The ubiquitous backpropagation algorithm requires sequential updates through the network introducing a locking problem. In addition, back-propagation relies on the transpose of for…
Efficient recurrent architectures through activity sparsity and sparse back-propagation through time
Anand Subramoney, Khaleelulla Khan Nazeer, Mark Schöne +2
Recurrent neural networks (RNNs) are well suited for solving sequence tasks in resource-constrained systems due to their expressivity and low computational requirements. However, t…