1 citations · 1 across the 6 of their papers we have counts for
5 papers
RAMAN: A Re-configurable and Sparse tinyML Accelerator for Inference on Edge
Adithya Krishna, Srikanth Rohit Nudurupati, Chandana D G +4
Deep Neural Network (DNN) based inference at the edge is challenging as these compute and data-intensive algorithms need to be implemented at low cost and low power while meeting t…
Neuromorphic Computing with AER using Time-to-Event-Margin Propagation
Madhuvanthi Srivatsav R, Shantanu Chakrabartty, Chetan Singh Thakur
Address-Event-Representation (AER) is a spike-routing protocol that allows the scaling of neuromorphic and spiking neural network (SNN) architectures to a size that is comparable t…
Multiplierless In-filter Computing for tinyML Platforms
Abhishek Ramdas Nair, Pallab Kumar Nath, Shantanu Chakrabartty +1
Wildlife conservation using continuous monitoring of environmental factors and biomedical classification, which generate a vast amount of sensor data, is a challenge due to limited…
Theoretical Insight into Batch Normalization: Data Dependant Auto-Tuning of Regularization Rate
Lakshmi Annamalai, Chetan Singh Thakur
Batch normalization is widely used in deep learning to normalize intermediate activations. Deep networks suffer from notoriously increased training complexity, mandating careful in…
A Trainable Neuromorphic Integrated Circuit that Exploits Device Mismatch
Chetan Singh Thakur, Runchun Wang, Tara Julia Hamilton +2
Random device mismatch that arises as a result of scaling of the CMOS (complementary metal-oxide semi-conductor) technology into the deep submicron regime degrades the accuracy of…