508 research outputs found
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
Two Stream LSTM: A Deep Fusion Framework for Human Action Recognition
In this paper we address the problem of human action recognition from video
sequences. Inspired by the exemplary results obtained via automatic feature
learning and deep learning approaches in computer vision, we focus our
attention towards learning salient spatial features via a convolutional neural
network (CNN) and then map their temporal relationship with the aid of
Long-Short-Term-Memory (LSTM) networks. Our contribution in this paper is a
deep fusion framework that more effectively exploits spatial features from CNNs
with temporal features from LSTM models. We also extensively evaluate their
strengths and weaknesses. We find that by combining both the sets of features,
the fully connected features effectively act as an attention mechanism to
direct the LSTM to interesting parts of the convolutional feature sequence. The
significance of our fusion method is its simplicity and effectiveness compared
to other state-of-the-art methods. The evaluation results demonstrate that this
hierarchical multi stream fusion method has higher performance compared to
single stream mapping methods allowing it to achieve high accuracy
outperforming current state-of-the-art methods in three widely used databases:
UCF11, UCFSports, jHMDB.Comment: Published as a conference paper at WACV 201
- …