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Found 11 papers, 3 papers with code
Hardware-aware training of models with synaptic delays for digital event-driven neuromorphic processors
In this work, we propose a framework to train and deploy, in digital neuromorphic hardware, highly performing spiking neural network models (SNNs) where apart from the synaptic weights, the per-synapse delays are also co-optimized.
NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.
Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time
When combined with a novel dynamic spiking neuron model, the Liquid-Time-Constant neuron, we show that SNNs trained with FPTT outperform online BPTT approximations, and approach or exceed offline BPTT accuracy on temporal classification tasks.
Design of Many-Core Big Little μBrain for Energy-Efficient Embedded Neuromorphic Computing
We propose a system software framework called SentryOS to map SDCNN inference applications to the proposed design.
Evolved neuromorphic radar-based altitude controller for an autonomous open-source blimp
Robotic airships offer significant advantages in terms of safety, mobility, and extended flight times.
Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks
Inspired by more detailed modeling of biological neurons, Spiking neural networks (SNNs) have been investigated both as more biologically plausible and potentially more powerful models of neural computation, and also with the aim of extracting biological neurons' energy efficiency; the performance of such networks however has remained lacking compared to classical artificial neural networks (ANNs).
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Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks
The emergence of brain-inspired neuromorphic computing as a paradigm for edge AI is motivating the search for high-performance and efficient spiking neural networks to run on this hardware.
PRED18: Dataset and Further Experiments with DAVIS Event Camera in Predator-Prey Robot Chasing
Conventional vision CNNs are driven by camera frames at constant sample rate, thus achieving a fixed latency and power consumption tradeoff.
NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps
By exploiting sparsity, NullHop achieves an efficiency of 368%, maintains over 98% utilization of the MAC units, and achieves a power efficiency of over 3TOp/s/W in a core area of 6. 3mm$^2$.
Steering a Predator Robot using a Mixed Frame/Event-Driven Convolutional Neural Network
The CNN is trained and run on data from a Dynamic and Active Pixel Sensor (DAVIS) mounted on a Summit XL robot (the predator), which follows another one (the prey).
Real time unsupervised learning of visual stimuli in neuromorphic VLSI systems
The initial network state is dictated by the stimulus, and relaxation to the attractor state implements the retrieval of the corresponding memorized prototypical pattern.
