https://ieeexplore.ieee.org/document/8552117The hardware implementation of neuro-inspired machine learning algorithms for near sensor processing on edge devices is an open problem. In this work, we propose a solution to written word recognition problem related to sequence learning tasks with images. Applying a theoretical framework of neocortex functionality as a sequence learning algorithm on a hardware implementation of Hierarchical Temporal Memory (HTM), we test the potential use of HTM in near-sensor on-chip natural language processing for text/symbol recognition
A word recognition architecture based on a network of neural associative memories and hidden Markov ...
© 2021 The Authors.The dynamic processing of optoelectronic signals carrying temporal and sequential...
Neuromorphic engineering pursues the design of electronic systems emulating function and structural ...
With recently advances in technology (hardware and software) there is an interest of humanity in hav...
This article investigates hardware implementation of hierarchical temporal memory (HTM), a brain-ins...
https://arxiv.org/abs/1803.05131Hierarchical temporal memory (HTM) is a neuromorphic algorithm that ...
At this time, great effort is being directed toward developing problem-solving technology that mimic...
It is herein proposed a handwritten digit recognition system which biologically inspired of the larg...
https://ieeexplore.ieee.org/document/8471012This paper presents a survey of the currently available ...
The on-chip implementation of learning algorithms would accelerate the training of neural networks i...
Biological brains exhibit a remarkable capacity to recognise real-world patterns effectively. Despit...
This Master Thesis has been performed at the Department of Electrical Engineering, Division of Elect...
In this study learning reinforcement and noise rejection of a spatial pooler was examined, the first...
Hierarchical Temporal Memory (HTM) serves as a practical implementation of the memory prediction the...
Machine learning models for sequence learning and processing often suffer from high energy consumpti...
A word recognition architecture based on a network of neural associative memories and hidden Markov ...
© 2021 The Authors.The dynamic processing of optoelectronic signals carrying temporal and sequential...
Neuromorphic engineering pursues the design of electronic systems emulating function and structural ...
With recently advances in technology (hardware and software) there is an interest of humanity in hav...
This article investigates hardware implementation of hierarchical temporal memory (HTM), a brain-ins...
https://arxiv.org/abs/1803.05131Hierarchical temporal memory (HTM) is a neuromorphic algorithm that ...
At this time, great effort is being directed toward developing problem-solving technology that mimic...
It is herein proposed a handwritten digit recognition system which biologically inspired of the larg...
https://ieeexplore.ieee.org/document/8471012This paper presents a survey of the currently available ...
The on-chip implementation of learning algorithms would accelerate the training of neural networks i...
Biological brains exhibit a remarkable capacity to recognise real-world patterns effectively. Despit...
This Master Thesis has been performed at the Department of Electrical Engineering, Division of Elect...
In this study learning reinforcement and noise rejection of a spatial pooler was examined, the first...
Hierarchical Temporal Memory (HTM) serves as a practical implementation of the memory prediction the...
Machine learning models for sequence learning and processing often suffer from high energy consumpti...
A word recognition architecture based on a network of neural associative memories and hidden Markov ...
© 2021 The Authors.The dynamic processing of optoelectronic signals carrying temporal and sequential...
Neuromorphic engineering pursues the design of electronic systems emulating function and structural ...