1 code implementation • 19 Sep 2021 • Chun Tao, Deboleena Roy, Indranil Chakraborty, Kaushik Roy
First, we study the noise stability of such networks on unperturbed inputs and observe that internal activations of adversarially trained networks have lower Signal-to-Noise Ratio (SNR), and are sensitive to noise compared to vanilla networks.
no code implementations • 27 Aug 2020 • Deboleena Roy, Indranil Chakraborty, Timur Ibrayev, Kaushik Roy
The increasing computational demand of Deep Learning has propelled research in special-purpose inference accelerators based on emerging non-volatile memory (NVM) technologies.
1 code implementation • 4 Jun 2019 • Indranil Chakraborty, Deboleena Roy, Isha Garg, Aayush Ankit, Kaushik Roy
The `Internet of Things' has brought increased demand for AI-based edge computing in applications ranging from healthcare monitoring systems to autonomous vehicles.
no code implementations • 24 May 2019 • Deboleena Roy, Priyadarshini Panda, Kaushik Roy
The spiking autoencoders are benchmarked on MNIST and Fashion-MNIST and achieve very low reconstruction loss, comparable to ANNs.
no code implementations • 1 Feb 2019 • Indranil Chakraborty, Deboleena Roy, Aayush Ankit, Kaushik Roy
In this work, we propose extremely quantized hybrid network architectures with both binary and full-precision sections to emulate the classification performance of full-precision networks while ensuring significant energy efficiency and memory compression.
no code implementations • 1 Jul 2018 • Amogh Agrawal, Akhilesh Jaiswal, Deboleena Roy, Bing Han, Gopalakrishnan Srinivasan, Aayush Ankit, Kaushik Roy
In this paper, we demonstrate how deep binary networks can be accelerated in modified von-Neumann machines by enabling binary convolutions within the SRAM array.
Emerging Technologies
1 code implementation • 15 Feb 2018 • Deboleena Roy, Priyadarshini Panda, Kaushik Roy
Over the past decade, Deep Convolutional Neural Networks (DCNNs) have shown remarkable performance in most computer vision tasks.