Scaled-Time-Attention Robust Edge Network

by   Richard Lau, et al.

This paper describes a systematic approach towards building a new family of neural networks based on a delay-loop version of a reservoir neural network. The resulting architecture, called Scaled-Time-Attention Robust Edge (STARE) network, exploits hyper dimensional space and non-multiply-and-add computation to achieve a simpler architecture, which has shallow layers, is simple to train, and is better suited for Edge applications, such as Internet of Things (IoT), over traditional deep neural networks. STARE incorporates new AI concepts such as Attention and Context, and is best suited for temporal feature extraction and classification. We demonstrate that STARE is applicable to a variety of applications with improved performance and lower implementation complexity. In particular, we showed a novel way of applying a dual-loop configuration to detection and identification of drone vs bird in a counter Unmanned Air Systems (UAS) detection application by exploiting both spatial (video frame) and temporal (trajectory) information. We also demonstrated that the STARE performance approaches that of a State-of-the-Art deep neural network in classifying RF modulations, and outperforms Long Short-term Memory (LSTM) in a special case of Mackey Glass time series prediction. To demonstrate hardware efficiency, we designed and developed an FPGA implementation of the STARE algorithm to demonstrate its low-power and high-throughput operations. In addition, we illustrate an efficient structure for integrating a massively parallel implementation of the STARE algorithm for ASIC implementation.


page 4

page 9

page 14

page 15

page 16

page 17

page 18

page 19


RTFN: A Robust Temporal Feature Network for Time Series Classification

Time series data usually contains local and global patterns. Most of the...

A Reconfigurable Low Power High Throughput Architecture for Deep Network Training

General purpose computing systems are used for a large variety of applic...

A High GOPs/Slice Time Series Classifier for Portable and Embedded Biomedical Applications

Modern wearable rehabilitation devices and health support systems operat...

Hardware Synthesis of State-Space Equations; Application to FPGA Implementation of Shallow and Deep Neural Networks

Nowadays, shallow and deep Neural Networks (NNs) have vast applications ...

Feature Extraction and Classification Based on Spatial-Spectral ConvLSTM Neural Network for Hyperspectral Images

In recent years, deep learning has presented a great advance in hyperspe...

Artificial Neural Network for Cybersecurity: A Comprehensive Review

Cybersecurity is a very emerging field that protects systems, networks, ...

Deep Delay Loop Reservoir Computing for Specific Emitter Identification

Current AI systems at the tactical edge lack the computational resources...

Please sign up or login with your details

Forgot password? Click here to reset