A survey on hyperdimensional computing aka vector symbolic architectures, part ii: Applications, cognitive models, and challenges
This is Part II of the two-part comprehensive survey devoted to a computing framework most
commonly known under the names Hyperdimensional Computing and Vector Symbolic …
commonly known under the names Hyperdimensional Computing and Vector Symbolic …
Vector symbolic architectures as a computing framework for emerging hardware
This article reviews recent progress in the development of the computing framework vector
symbolic architectures (VSA)(also known as hyperdimensional computing). This framework …
symbolic architectures (VSA)(also known as hyperdimensional computing). This framework …
Advancing neuromorphic computing with loihi: A survey of results and outlook
Deep artificial neural networks apply principles of the brain's information processing that led
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …
Diet-snn: A low-latency spiking neural network with direct input encoding and leakage and threshold optimization
Bioinspired spiking neural networks (SNNs), operating with asynchronous binary signals (or
spikes) distributed over time, can potentially lead to greater computational efficiency on …
spikes) distributed over time, can potentially lead to greater computational efficiency on …
Diet-snn: Direct input encoding with leakage and threshold optimization in deep spiking neural networks
Bio-inspired spiking neural networks (SNNs), operating with asynchronous binary signals
(or spikes) distributed over time, can potentially lead to greater computational efficiency on …
(or spikes) distributed over time, can potentially lead to greater computational efficiency on …
A survey on neuromorphic computing: Models and hardware
The explosion of “big data” applications imposes severe challenges of speed and scalability
on traditional computer systems. As the performance of traditional Von Neumann machines …
on traditional computer systems. As the performance of traditional Von Neumann machines …
Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes
Spike-based neuromorphic hardware promises to reduce the energy consumption of image
classification and other deep-learning applications, particularly on mobile phones and other …
classification and other deep-learning applications, particularly on mobile phones and other …
Beyond classification: Directly training spiking neural networks for semantic segmentation
Spiking neural networks (SNNs) have recently emerged as the low-power alternative to
artificial neural networks (ANNs) because of their sparse, asynchronous, and binary event …
artificial neural networks (ANNs) because of their sparse, asynchronous, and binary event …
Brain-inspired multimodal hybrid neural network for robot place recognition
Place recognition is an essential spatial intelligence capability for robots to understand and
navigate the world. However, recognizing places in natural environments remains a …
navigate the world. However, recognizing places in natural environments remains a …
Variable binding for sparse distributed representations: Theory and applications
Variable binding is a cornerstone of symbolic reasoning and cognition. But how binding can
be implemented in connectionist models has puzzled neuroscientists, cognitive …
be implemented in connectionist models has puzzled neuroscientists, cognitive …