217 citations · 377 across the 8 of their papers we have counts for
10 papers
Analog, In-memory Compute Architectures for Artificial Intelligence
Patrick Bowen, Guy Regev, Nir Regev +3
This paper presents an analysis of the fundamental limits on energy efficiency in both digital and analog in-memory computing architectures, and compares their performance to singl…
Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning
Georgios Detorakis, Sadique Sheik, Charles Augustine +6
Embedded, continual learning for autonomous and adaptive behavior is a key application of neuromorphic hardware. However, neuromorphic implementations of embedded learning at large…
Hardware-efficient on-line learning through pipelined truncated-error backpropagation in binary-state networks
Hesham Mostafa, Bruno Pedroni, Sadique Sheik +1
Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significa…
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…
Mapping Generative Models onto a Network of Digital Spiking Neurons
Bruno U. Pedroni, Srinjoy Das, John V. Arthur +5
Stochastic neural networks such as Restricted Boltzmann Machines (RBMs) have been successfully used in applications ranging from speech recognition to image classification. Inferen…
Gibbs Sampling with Low-Power Spiking Digital Neurons
Srinjoy Das, Bruno Umbria Pedroni, Paul Merolla +6
Restricted Boltzmann Machines and Deep Belief Networks have been successfully used in a wide variety of applications including image classification and speech recognition. Inferenc…
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…