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Found 10 papers, 3 papers with code
Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline
This is due to the increasing number of smart devices that require sensory processing for their application on the edge.
NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking
The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles.
EXODUS: Stable and Efficient Training of Spiking Neural Networks
In this paper, (i) we modify SLAYER and design an algorithm called EXODUS, that accounts for the neuron reset mechanism and applies the Implicit Function Theorem (IFT) to calculate the correct gradients (equivalent to those computed by BPTT), (ii) we eliminate the need for ad-hoc scaling of gradients, thus, reducing the training complexity tremendously, (iii) we demonstrate, via computer simulations, that EXODUS is numerically stable and achieves a comparable or better performance than SLAYER especially in various tasks with SNNs that rely on temporal features.
WaveSense: Efficient Temporal Convolutions with Spiking Neural Networks for Keyword Spotting
We extend this idea to WaveSense, a spiking neural network inspired by the WaveNet architecture.
Adversarial Attacks on Spiking Convolutional Neural Networks for Event-based Vision
Event-based dynamic vision sensors provide very sparse output in the form of spikes, which makes them suitable for low-power applications.
Optimizing the energy consumption of spiking neural networks for neuromorphic applications
We demonstrate first that quantization-aware training of CNNs leads to better accuracy in SNNs.
Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning
Embedded, continual learning for autonomous and adaptive behavior is a key application of neuromorphic hardware.
Hardware-efficient on-line learning through pipelined truncated-error backpropagation in binary-state networks
In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation.
Membrane-Dependent Neuromorphic Learning Rule for Unsupervised Spike Pattern Detection
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 capabilities to Spiking Neural Networks.
Forward Table-Based Presynaptic Event-Triggered Spike-Timing-Dependent Plasticity
We present a novel method for realizing both causal and acausal weight updates using only forward lookup access of the synaptic connectivity table, permitting memory-efficient implementation.
