8 citations · 30 across the 6 of their papers we have counts for
6 papers
Language Modeling using LMUs: 10x Better Data Efficiency or Improved Scaling Compared to Transformers
Narsimha Chilkuri, Eric Hunsberger, Aaron Voelker +2
Recent studies have demonstrated that the performance of transformers on the task of language modeling obeys a power-law relationship with model size over six orders of magnitude.…
Hardware Aware Training for Efficient Keyword Spotting on General Purpose and Specialized Hardware
Peter Blouw, Gurshaant Malik, Benjamin Morcos +2
Keyword spotting (KWS) provides a critical user interface for many mobile and edge applications, including phones, wearables, and cars. As KWS systems are typically 'always on', ma…
A short letter on the dot product between rotated Fourier transforms
Aaron R. Voelker
Spatial Semantic Pointers (SSPs) have recently emerged as a powerful tool for representing and transforming continuous space, with numerous applications to cognitive modelling and…
A Spike in Performance: Training Hybrid-Spiking Neural Networks with Quantized Activation Functions
Aaron R. Voelker, Daniel Rasmussen, Chris Eliasmith
The machine learning community has become increasingly interested in the energy efficiency of neural networks. The Spiking Neural Network (SNN) is a promising approach to energy-ef…
Point Neurons with Conductance-Based Synapses in the Neural Engineering Framework
Andreas Stöckel, Aaron R. Voelker, Chris Eliasmith
The mathematical model underlying the Neural Engineering Framework (NEF) expresses neuronal input as a linear combination of synaptic currents. However, in biology, synapses are no…
Methods for applying the Neural Engineering Framework to neuromorphic hardware
Aaron R. Voelker, Chris Eliasmith
We review our current software tools and theoretical methods for applying the Neural Engineering Framework to state-of-the-art neuromorphic hardware. These methods can be used to i…