21 citations · 30 across the 12 of their papers we have counts for
13 papers
Lock-in EP: An In-Situ Training Algorithm for Oscillatory Hardware
Sowjanya Tammali, Wilkie Olin-Ammentorp
Analog hardware platforms offer the potential to reduce energy consumption over digital architectures, but in order to succeed, large-scale analog systems must also be able to oper…
Phase State Space Models: Parallel, Surrogate-Free Training of Spiking Networks
Wilkie Olin-Ammentorp
State-space models (SSMs) provide a powerful theoretical framework to enable parallel training of recurrent networks. We expand on previous work adapting SSMs to spiking models to…
More than MACs: Exploring the Role of Neuromorphic Engineering in the Age of LLMs
Wilkie Olin-Ammentorp
The introduction of large language models has significantly expanded global demand for computing; addressing this growing demand requires novel approaches that introduce new capabi…
Co-Design of 2D Heterojunctions for Data Filtering in Tracking Systems
Tupendra Oli, Wilkie Olin-Ammentorp, Xingfu Wu +5
As particle physics experiments evolve to achieve higher energies and resolutions, handling the massive data volumes produced by silicon pixel detectors, which are used for charged…
Modeling Performance of Data Collection Systems for High-Energy Physics
Wilkie Olin-Ammentorp, Xingfu Wu, Andrew A. Chien
Exponential increases in scientific experimental data are outstripping the rate of progress in silicon technology. As a result, heterogeneous combinations of architectures and proc…
Hyperdimensional Computing Provides a Programming Paradigm for Oscillatory Systems
Wilkie Olin-Ammentorp
The increasing difficulty in continued development of digital electronic logic has led to a renewed interest in alternative approaches. Oscillatory computing is one such approach t…
Sparsifying Spiking Networks through Local Rhythms
Wilkie Olin-Ammentorp
It has been well-established that within conventional neural networks, many of the values produced at each layer are zero. In this work, I demonstrate that spiking neural networks…
Residual and Attentional Architectures for Vector-Symbols
Wilkie Olin-Ammentorp Maxim Bazhenov
Vector-symbolic architectures (VSAs) provide methods for computing which are highly flexible and carry unique advantages. Concepts in VSAs are represented by 'symbols,' long vector…
Bridge Networks: Relating Inputs through Vector-Symbolic Manipulations
Wilkie Olin-Ammentorp, Maxim Bazhenov
Despite rapid progress, current deep learning methods face a number of critical challenges. These include high energy consumption, catastrophic forgetting, dependance on global los…
Deep Phasor Networks: Connecting Conventional and Spiking Neural Networks
Wilkie Olin-Ammentorp, Maxim Bazhenov
In this work, we extend standard neural networks by building upon an assumption that neuronal activations correspond to the angle of a complex number lying on the unit circle, or '…
A Dual-Memory Architecture for Reinforcement Learning on Neuromorphic Platforms
Wilkie Olin-Ammentorp, Yury Sokolov, Maxim Bazhenov
Reinforcement learning (RL) is a foundation of learning in biological systems and provides a framework to address numerous challenges with real-world artificial intelligence applic…
Cellular Memristive-Output Reservoir (CMOR)
Wilkie Olin-Ammentorp, Karsten Beckmann, Nathaniel C. Cady
Reservoir computing is a subfield of machine learning in which a complex system, or 'reservoir,' uses complex internal dynamics to non-linearly project an input into a higher-dimen…
Stochasticity and Robustness in Spiking Neural Networks
Wilkie Olin-Ammentorp, Karsten Beckmann, Catherine D. Schuman +2
Artificial neural networks normally require precise weights to operate, despite their origins in biological systems, which can be highly variable and noisy. When implementing artif…