4 research outputs found
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. However, compared to classical neural
networks in deep learning, current spiking neural networks lack competitive
performance in compelling areas. Here, for sequential and streaming tasks, we
demonstrate how a novel type of adaptive spiking recurrent neural network
(SRNN) is able to achieve state-of-the-art performance compared to other
spiking neural networks and almost reach or exceed the performance of classical
recurrent neural networks (RNNs) while exhibiting sparse activity. From this,
we calculate a 100x energy improvement for our SRNNs over classical RNNs on
the harder tasks. To achieve this, we model standard and adaptive
multiple-timescale spiking neurons as self-recurrent neural units, and leverage
surrogate gradients and auto-differentiation in the PyTorch Deep Learning
framework to efficiently implement backpropagation-through-time, including
learning of the important spiking neuron parameters to adapt our spiking
neurons to the tasks.Comment: 11 pages,5 figure
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. However, compared to classical neural networks in deep learning, current spiking neural networks lack competitive performance in compelling areas. Here, for sequential and streaming tasks, we demonstrate how a novel type of adaptive spiking recurrent neural network (SRNN) is able to achieve state-of-the-art performance compared to other spiking neural networks and almost reach or exceed the performance of classical recurrent neural networks (RNNs) while exhibiting sparse activity. From this, we calculate a > 100x energy improvement for our SRNNs over classical RNNs on the harder tasks. To achieve this, we model standard and adaptive multiple-timescale spiking neurons as self-recurrent neural units, and leverage surrogate gradients and auto-differentiation in the PyTorch Deep Learning framework to efficiently implement backpropagation-through-time, including learning of the important spiking neuron parameters to adapt our spiking neurons to the tasks
3D objects and scenes classification, recognition, segmentation, and reconstruction using 3D point cloud data: A review
Three-dimensional (3D) point cloud analysis has become one of the attractive
subjects in realistic imaging and machine visions due to its simplicity,
flexibility and powerful capacity of visualization. Actually, the
representation of scenes and buildings using 3D shapes and formats leveraged
many applications among which automatic driving, scenes and objects
reconstruction, etc. Nevertheless, working with this emerging type of data has
been a challenging task for objects representation, scenes recognition,
segmentation, and reconstruction. In this regard, a significant effort has
recently been devoted to developing novel strategies, using different
techniques such as deep learning models. To that end, we present in this paper
a comprehensive review of existing tasks on 3D point cloud: a well-defined
taxonomy of existing techniques is performed based on the nature of the adopted
algorithms, application scenarios, and main objectives. Various tasks performed
on 3D point could data are investigated, including objects and scenes
detection, recognition, segmentation and reconstruction. In addition, we
introduce a list of used datasets, we discuss respective evaluation metrics and
we compare the performance of existing solutions to better inform the
state-of-the-art and identify their limitations and strengths. Lastly, we
elaborate on current challenges facing the subject of technology and future
trends attracting considerable interest, which could be a starting point for
upcoming research studie