217 citations · 424 across the 13 of their papers we have counts for
15 papers
Active Inference in Hebbian Learning Networks
Ali Safa, Tim Verbelen, Lars Keuninckx +5
This work studies how brain-inspired neural ensembles equipped with local Hebbian plasticity can perform active inference (AIF) in order to control dynamical agents. A generative m…
Edge AI without Compromise: Efficient, Versatile and Accurate Neurocomputing in Resistive Random-Access Memory
Weier Wan, Rajkumar Kubendran, Clemens Schaefer +11
Realizing today's cloud-level artificial intelligence functionalities directly on devices distributed at the edge of the internet calls for edge hardware capable of processing mult…
Large-Scale Neuromorphic Spiking Array Processors: A quest to mimic the brain
Chetan Singh Thakur, Jamal Molin, Gert Cauwenberghs +12
Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuro…
Deep supervised learning using local errors
Hesham Mostafa, Vishwajith Ramesh, Gert Cauwenberghs
Error backpropagation is a highly effective mechanism for learning high-quality hierarchical features in deep networks. Updating the features or weights in one layer, however, requ…
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…
A learning framework for winner-take-all networks with stochastic synapses
Hesham Mostafa, Gert Cauwenberghs
Many recent generative models make use of neural networks to transform the probability distribution of a simple low-dimensional noise process into the complex distribution of the d…
Membrane-Dependent Neuromorphic Learning Rule for Unsupervised Spike Pattern Detection
Sadique Sheik, Somnath Paul, Charles Augustine +1
Several learning rules for synaptic plasticity, that depend on either spike timing or internal state variables, have been proposed in the past imparting varying computational capab…
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…
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…
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…