A memristive spiking neural network circuit with selective supervised attention algorithm
Spiking neural networks (SNNs) are biologically plausible and computationally powerful.
The current computing systems based on the von Neumann architecture are almost the …
The current computing systems based on the von Neumann architecture are almost the …
Hire-snn: Harnessing the inherent robustness of energy-efficient deep spiking neural networks by training with crafted input noise
Low-latency deep spiking neural networks (SNNs) have become a promising alternative to
conventional artificial neural networks (ANNs) because of their potential for increased …
conventional artificial neural networks (ANNs) because of their potential for increased …
A processing-in-pixel-in-memory paradigm for resource-constrained tinyml applications
The demand to process vast amounts of data generated from state-of-the-art high resolution
cameras has motivated novel energy-efficient on-device AI solutions. Visual data in such …
cameras has motivated novel energy-efficient on-device AI solutions. Visual data in such …
Training much deeper spiking neural networks with a small number of time-steps
Abstract Spiking Neural Network (SNN) is a promising energy-efficient neural architecture
when implemented on neuromorphic hardware. The Artificial Neural Network (ANN) to SNN …
when implemented on neuromorphic hardware. The Artificial Neural Network (ANN) to SNN …
Can deep neural networks be converted to ultra low-latency spiking neural networks?
Spiking neural networks (SNNs), that operate via binary spikes distributed over time, have
emerged as a promising energy efficient ML paradigm for resource-constrained devices …
emerged as a promising energy efficient ML paradigm for resource-constrained devices …
ACE-SNN: Algorithm-hardware co-design of energy-efficient & low-latency deep spiking neural networks for 3d image recognition
High-quality 3D image recognition is an important component of many vision and robotics
systems. However, the accurate processing of these images requires the use of compute …
systems. However, the accurate processing of these images requires the use of compute …
Adaptive spatiotemporal neural networks through complementary hybridization
Processing spatiotemporal data sources with both high spatial dimension and rich temporal
information is a ubiquitous need in machine intelligence. Recurrent neural networks in the …
information is a ubiquitous need in machine intelligence. Recurrent neural networks in the …
BP-SRM: A directly training algorithm for spiking neural network constructed by spike response model
Spiking neural networks (SNNs) have attracted widespread attention due to their unique bio-
interpretability and low-power properties, but the non-differentiability of discrete spike …
interpretability and low-power properties, but the non-differentiability of discrete spike …
Hoyer regularizer is all you need for ultra low-latency spiking neural networks
Spiking Neural networks (SNN) have emerged as an attractive spatio-temporal computing
paradigm for a wide range of low-power vision tasks. However, state-of-the-art (SOTA) SNN …
paradigm for a wide range of low-power vision tasks. However, state-of-the-art (SOTA) SNN …
Towards energy-efficient, low-latency and accurate spiking LSTMs
Spiking Neural Networks (SNNs) have emerged as an attractive spatio-temporal computing
paradigm for complex vision tasks. However, most existing works yield models that require …
paradigm for complex vision tasks. However, most existing works yield models that require …