A Survey of Neuromorphic Computing and Neural Networks in Hardware

19 May 20171 code implementation

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture.

Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks

NeurIPS 2018 1 code implementation

We evaluate the proposed HM2-BP algorithm by training deep fully connected and convolutional SNNs based on the static MNIST [14] and dynamic neuromorphic N-MNIST [26].


Bioinspired Visual Motion Estimation

31 Oct 20151 code implementation

Visual motion estimation is a computationally intensive, but important task for sighted animals.


Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware

4 Dec 20181 code implementation

Using Intel's Loihi neuromorphic research chip and ABR's Nengo Deep Learning toolkit, we analyze the inference speed, dynamic power consumption, and energy cost per inference of a two-layer neural network keyword spotter trained to recognize a single phrase.


Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE)

27 Nov 20182 code implementations

A relatively smaller body of work, however, discusses similarities between learning dynamics employed in deep artificial neural networks and synaptic plasticity in spiking neural networks.

EVDodgeNet: Deep Dynamic Obstacle Dodging with Event Cameras

7 Jun 20191 code implementation

To our knowledge, this is the first deep learning -- based solution to the problem of dynamic obstacle avoidance using event cameras on a quadrotor.


Passive nonlinear dendritic interactions as a general computational resource in functional spiking neural networks

26 Apr 20192 code implementations

Nonlinear interactions in the dendritic tree play a key role in neural computation.

Digital Electronics and Analog Photonics for Convolutional Neural Networks (DEAP-CNNs)

23 Apr 20191 code implementation

Convolutional Neural Networks (CNNs) are powerful and highly ubiquitous tools for extracting features from large datasets for applications such as computer vision and natural language processing.

The Probabilistic Fault Tolerance of Neural Networks in the Continuous Limit

ICLR 2020 1 code implementation

We study fault tolerance of neural networks subject to small random neuron/weight crash failures in a probabilistic setting.

Single Headed Attention RNN: Stop Thinking With Your Head

26 Nov 20191 code implementation

The leading approaches in language modeling are all obsessed with TV shows of my youth - namely Transformers and Sesame Street.