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Arindam Basu
Nanyang Technological University
Machine learningNeuromorphic engineeringElectronic engineeringComputer scienceCMOS
121Publications
18H-index
1,263Citations
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Publications 147
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#1Sumon Kumar Bose (NTU: Nanyang Technological University)H-Index: 1
#2Vivek Mohan (NTU: Nanyang Technological University)
Last. Arindam Basu (NTU: Nanyang Technological University)H-Index: 18
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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 on a binary image generated from neuromorphic vision sensors. Implemented in TSMC 65nm process, the proposed architecture enables approximately 2000X energy savings (approximately 222X from IMC) compared to a digital implementation when tested with the video recordings from a DAVIS sensor and achieves a peak throughpu...
Last. Arindam Basu (NTU: Nanyang Technological University)H-Index: 18
view all 6 authors...
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 over traditional imagers in this domain, the existing NVS systems either do not meet the power constraints or have not demonstrated end-to-end system performance. To address this, we improve on a recently proposed hybrid event-frame approach by using morphological image processing algorithms for region proposal and add...
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 and pulmonary diseases. In this work we propose a deep CNN-RNN model that classifies respiratory sounds based on Mel-spectrograms. We also implement a patient specific model tuning strategy that first screens respiratory patients and then builds patient specific classification models using limited patient data for reli...
1 CitationsSource
#1Govind NarasimmanH-Index: 2
#2Joydeep BasuH-Index: 5
Last. Arindam BasuH-Index: 18
view all 7 authors...
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This brief presents an energy-efficient collocated random access memory (CRAM) featuring charge based computing. A 9Tbit-cell-based collocated DRAM and SRAM together with a 2-dimensional charge diffusion paths topology is used to store the input binary image data and compute locally. The computing network performs both denoising approximating a nearest-neighbor median filter for background noise and region filling for the fragmented objects. Compared to conventional in-memory computing (IMC) ...
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#1Sumon Kumar BoseH-Index: 1
#2Jyotibdha AcharyaH-Index: 1
Last. Arindam BasuH-Index: 18
view all 3 authors...
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 see that there is no clear consensus but each system has one or more of the following features:(1) Analog computing (2) Non vonNeumann Architecture and low-precision digital processing (3) Spiking Neural Networks (SNN) with components closely related to biology. We compare recent machine learning accelerator chips to sh...
This paper presents a novel sparse ensemble based machine learning approach to enhance robustness of intracortical Brain Machine Interfaces (iBMIs) in the face of non-stationary distribution of input neural data across time. Each classifier in the ensemble is trained on a randomly sampled (with replacement) set of input channels. These sparse connections ensure that with a high chance, few of the base classifiers should be less affected by the variations in some of the recording channels. We hav...
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#1Laxmi Ravi Iyer (NTU: Nanyang Technological University)H-Index: 2
#2Arindam Basu (NTU: Nanyang Technological University)H-Index: 18
The creation of useful categories from data is an important cognitive ability,and from the extensive research on categorization, it is now known that the brain has distinct systems for category learning. In this paper, we present the first spiking neural network (SNN) model of human category learning.Here categories are combinations of features - such categories are observed in the prefrontal cortex (PFC). The system follows an architecture commonly used to model the cortex - features are arrang...
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#1Sumon Kumar BoseH-Index: 1
#2Bapi KarH-Index: 1
Last. Arindam BasuH-Index: 18
view all 7 authors...
To overcome the energy and bandwidth limitations of traditional IoT systems, “edge computing” or information extraction at the sensor node has become popular. However, now it is important to create very low energy information extraction or pattern recognition systems. In this paper, we present an approximate computing method to reduce the computation energy of a specific type of IoT system used for anomaly detection (e.g. in predictive maintenance, epileptic seizure detection, etc). Termed as An...
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#1Shoeb Shaikh (NTU: Nanyang Technological University)H-Index: 1
#2Rosa Q. SoH-Index: 8
Last. Arindam Basu (NTU: Nanyang Technological University)H-Index: 18
view all 5 authors...
This paper presents application of Banditron - an online reinforcement learning algorithm (RL) in a discrete state intra-cortical Brain Machine Interface (iBMI) setting. We have analyzed two datasets from non-human primates (NHPs) - NHPA and NHP B each performing a 4-option discrete control task over a total of 8 days. Results show average improvements of≈15%,6% in NHP A and 15%,21% in NHP B overstate of the art algorithms - Hebbian Reinforcement Learning(HRL) and Attention Gated Reinforcement L...
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