A memristor-based learning engine for synaptic trace-based online learning
The memristor has been extensively used to facilitate the synaptic online learning of brain-
inspired spiking neural networks (SNNs). However, the current memristor-based work can …
inspired spiking neural networks (SNNs). However, the current memristor-based work can …
Efficient reward-based structural plasticity on a SpiNNaker 2 prototype
Advances in neuroscience uncover the mechanisms employed by the brain to efficiently
solve complex learning tasks with very limited resources. However, the efficiency is often lost …
solve complex learning tasks with very limited resources. However, the efficiency is often lost …
A fixed point exponential function accelerator for a neuromorphic many-core system
Many models of spiking neural networks heavily rely on exponential waveforms. On
neuromorphic multiprocessor systems like SpiNNaker, they have to be approximated by …
neuromorphic multiprocessor systems like SpiNNaker, they have to be approximated by …
Mapping the BCPNN learning rule to a memristor model
The Bayesian Confidence Propagation Neural Network (BCPNN) has been implemented in
a way that allows mapping to neural and synaptic processes in the human cortexandhas …
a way that allows mapping to neural and synaptic processes in the human cortexandhas …
Dynamic voltage and frequency scaling for neuromorphic many-core systems
We present a dynamic voltage and frequency scaling technique within SoCs for per-core
power management: the architecture allows for individual, self triggered performance-level …
power management: the architecture allows for individual, self triggered performance-level …
Benchmarking Hebbian learning rules for associative memory
Associative memory or content addressable memory is an important component function in
computer science and information processing and is a key concept in cognitive and …
computer science and information processing and is a key concept in cognitive and …
eBrainII: a 3 kW realtime custom 3D DRAM integrated ASIC implementation of a biologically plausible model of a human scale cortex
Abstract The Artificial Neural Networks (ANNs), like CNN/DNN and LSTM, are not
biologically plausible. Despite their initial success, they cannot attain the cognitive …
biologically plausible. Despite their initial success, they cannot attain the cognitive …
Approximate fixed-point elementary function accelerator for the spinnaker-2 neuromorphic chip
M Mikaitis, DR Lester, D Shang… - 2018 IEEE 25th …, 2018 - ieeexplore.ieee.org
Neuromorphic chips are used to model biologically inspired Spiking-Neural-Networks
(SNNs) where most models are based on differential equations. Equations for most SNN …
(SNNs) where most models are based on differential equations. Equations for most SNN …
Large-scale simulations of plastic neural networks on neuromorphic hardware
SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale
spiking neural networks at speeds close to biological real-time. Rather than using bespoke …
spiking neural networks at speeds close to biological real-time. Rather than using bespoke …