163 citations · 218 across the 17 of their papers we have counts for
7 papers · 1 filter
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…
ADIC: Anomaly Detection Integrated Circuit in 65nm CMOS utilizing Approximate Computing
Bapi Kar, Pradeep Kumar Gopalakrishnan, Sumon Kumar Bose +2
In this paper, we present a low-power anomaly detection integrated circuit (ADIC) based on a one-class classifier (OCC) neural network. The ADIC achieves low-power operation throug…
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…
A 75kb SRAM in 65nm CMOS for In-Memory Computing Based Neuromorphic Image Denoising
Sumon Kumar Bose, Vivek Mohan, Arindam Basu
This paper presents an in-memory computing (IMC) architecture for image denoising. The proposed SRAM based in-memory processing framework works in tandem with approximate computing…
HyNNA: Improved Performance for Neuromorphic Vision Sensor based Surveillance using Hybrid Neural Network Architecture
Deepak Singla, Soham Chatterjee, Lavanya Ramapantulu +3
Applications in the Internet of Video Things (IoVT) domain have very tight constraints with respect to power and area. While neuromorphic vision sensors (NVS) may offer advantages…