163 citations · 172 across the 7 of their papers we have counts for
7 papers
A pJ/cycle Differential Ring Oscillator in nm CMOS for Robust Neurocomputing
Xueyong Zhang, Jyotibdha Acharya, Arindam Basu
This paper presents a low-area and low-power consumption CMOS differential current controlled oscillator (CCO) for neuromorphic applications. The oscillation frequency is improved…
A Hybrid Neuromorphic Object Tracking and Classification Framework for Real-time Systems
Andres Ussa, Chockalingam Senthil Rajen, Deepak Singla +4
Deep learning inference that needs to largely take place on the 'edge' is a highly computational and memory intensive workload, making it intractable for low-power, embedded platfo…
Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning
Jyotibdha Acharya, Arindam Basu
The primary objective of this paper is to build classification models and strategies to identify breathing sound anomalies (wheeze, crackle) for automated diagnosis of respiratory…
Is my Neural Network Neuromorphic? Taxonomy, Recent Trends and Future Directions in Neuromorphic Engineering
Sumon Kumar Bose, Jyotibdha Acharya, Arindam Basu
In this paper, we review recent work published over the last 3 years under the umbrella of Neuromorphic engineering to analyze what are the common features among such systems. We s…
EBBIOT: A Low-complexity Tracking Algorithm for Surveillance in IoVT Using Stationary Neuromorphic Vision Sensors
Jyotibdha Acharya, Andres Ussa Caycedo, Vandana Reddy Padala +4
In this paper, we present EBBIOT-a novel paradigm for object tracking using stationary neuromorphic vision sensors in low-power sensor nodes for the Internet of Video Things (IoVT)…
A low-power end-to-end hybrid neuromorphic framework for surveillance applications
Andres Ussa, Luca Della Vedova, Vandana Reddy Padala +6
With the success of deep learning, object recognition systems that can be deployed for real-world applications are becoming commonplace. However, inference that needs to largely ta…