  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.
