18 citations · 72 across the 13 of their papers we have counts for
13 papers
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)…
Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models
Mark Schöne, Neeraj Mohan Sushma, Jingyue Zhuge +3
Event-based sensors are well suited for real-time processing due to their fast response times and encoding of the sensory data as successive temporal differences. These and other v…
Neuromorphic hardware for sustainable AI data centers
Bernhard Vogginger, Amirhossein Rostami, Vaibhav Jain +9
As humans advance toward a higher level of artificial intelligence, it is always at the cost of escalating computational resource consumption, which requires developing novel solut…
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…
Deploying Machine Learning Models to Ahead-of-Time Runtime on Edge Using MicroTVM
Chen Liu, Matthias Jobst, Liyuan Guo +3
In the past few years, more and more AI applications have been applied to edge devices. However, models trained by data scientists with machine learning frameworks, such as PyTorch…
Convolutional Neural Networks Quantization with Attention
Binyi Wu, Bernd Waschneck, Christian Georg Mayr
It has been proven that, compared to using 32-bit floating-point numbers in the training phase, Deep Convolutional Neural Networks (DCNNs) can operate with low precision during inf…
Robust Environment Perception for Automated Driving: A Unified Learning Pipeline for Visual-Infrared Object Detection
Mohsen Vadidar, Ali Kariminezhad, Christian Mayr +2
The RGB complementary metal-oxidesemiconductor (CMOS) sensor works within the visible light spectrum. Therefore it is very sensitive to environmental light conditions. On the contr…
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…
Raw Radar data based Object Detection and Heading estimation using Cross Attention
Ravi Kothari, Ali Kariminezhad, Christian Mayr +1
Radar is an inevitable part of the perception sensor set for autonomous driving functions. It plays a gap-filling role to complement the shortcomings of other sensors in diverse sc…
Time-coded Spiking Fourier Transform in Neuromorphic Hardware
Javier López-Randulfe, Nico Reeb, Negin Karimi +6
After several decades of continuously optimizing computing systems, the Moore's law is reaching itsend. However, there is an increasing demand for fast and efficient processing sys…