217 citations · 388 across the 6 of their papers we have counts for
8 papers
Memory Organization for Energy-Efficient Learning and Inference in Digital Neuromorphic Accelerators
Clemens JS Schaefer, Patrick Faley, Emre O Neftci +1
The energy efficiency of neuromorphic hardware is greatly affected by the energy of storing, accessing, and updating synaptic parameters. Various methods of memory organisation tar…
Training a Probabilistic Graphical Model with Resistive Switching Electronic Synapses
S. Burc Eryilmaz, Emre Neftci, Siddharth Joshi +6
Current large scale implementations of deep learning and data mining require thousands of processors, massive amounts of off-chip memory, and consume gigajoules of energy. Emerging…
Forward Table-Based Presynaptic Event-Triggered Spike-Timing-Dependent Plasticity
Bruno U. Pedroni, Sadique Sheik, Siddharth Joshi +5
Spike-timing-dependent plasticity (STDP) incurs both causal and acausal synaptic weight updates, for negative and positive time differences between pre-synaptic and post-synaptic s…
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware
Peter U. Diehl, Guido Zarrella, Andrew Cassidy +2
In recent years the field of neuromorphic low-power systems that consume orders of magnitude less power gained significant momentum. However, their wider use is still hindered by t…
TrueHappiness: Neuromorphic Emotion Recognition on TrueNorth
Peter U. Diehl, Bruno U. Pedroni, Andrew Cassidy +3
We present an approach to constructing a neuromorphic device that responds to language input by producing neuron spikes in proportion to the strength of the appropriate positive or…
Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines
Emre O. Neftci, Bruno U. Pedroni, Siddharth Joshi +2
Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling M…
Learning Non-deterministic Representations with Energy-based Ensembles
Maruan Al-Shedivat, Emre Neftci, Gert Cauwenberghs
The goal of a generative model is to capture the distribution underlying the data, typically through latent variables. After training, these variables are often used as a new repre…
Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems
Emre Neftci, Srinjoy Das, Bruno Pedroni +2
Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature…