Search Results for author: Kaushik Roy

Found 95 papers, 17 papers with code

Deep Spiking Neural Network: Energy Efficiency Through Time based Coding

no code implementations ECCV 2020 Bing Han, Kaushik Roy

The real-valued ReLU activations in ANN are encoded using the spike-times of the TSC neurons in the converted TSC-SNN.

Can Language Models Capture Graph Semantics? From Graphs to Language Model and Vice-Versa

no code implementations18 Jun 2022 Tarun Garg, Kaushik Roy, Amit Sheth

Knowledge Graphs are a great resource to capture semantic knowledge in terms of entities and relationships between the entities.

Knowledge Graphs Language Modelling

Process Knowledge-Infused AI: Towards User-level Explainability, Interpretability, and Safety

no code implementations9 Jun 2022 Amit Sheth, Manas Gaur, Kaushik Roy, Revathy Venkataraman, Vedant Khandelwal

For such applications, in addition to data and domain knowledge, the AI systems need to have access to and use the Process Knowledge, an ordered set of steps that the AI system needs to use or adhere to.

Food recommendation

A Co-design view of Compute in-Memory with Non-Volatile Elements for Neural Networks

no code implementations3 Jun 2022 Wilfried Haensch, Anand Raghunathan, Kaushik Roy, Bhaswar Chakrabart, Charudatta M. Phatak, Cheng Wang, Supratik Guha

In the second part, we review what is knows about the different new non-volatile memory materials and devices suited for compute in-memory, and discuss the outlook and challenges.

Norm-Scaling for Out-of-Distribution Detection

no code implementations6 May 2022 Deepak Ravikumar, Kaushik Roy

Therefore, applying a single threshold for all classes is not ideal since the same similarity score represents different uncertainties for different classes.

Out-of-Distribution Detection

Process Knowledge-infused Learning for Suicidality Assessment on Social Media

no code implementations26 Apr 2022 Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth

Improving the performance and natural language explanations of deep learning algorithms is a priority for adoption by humans in the real world.

Cryogenic Neuromorphic Hardware

no code implementations25 Mar 2022 Md Mazharul Islam, Shamiul Alam, Md Shafayat Hossain, Kaushik Roy, Ahmedullah Aziz

Since the human brain is the most compact and energy-efficient intelligent device known, it was intuitive to attempt to build an architecture that could mimic our brain, and so the chase for neuromorphic computing began.

Encoding Hierarchical Information in Neural Networks helps in Subpopulation Shift

no code implementations20 Dec 2021 Amitangshu Mukherjee, Isha Garg, Kaushik Roy

We show that learning in this structured hierarchical manner results in networks that are more robust against subpopulation shifts, with an improvement up to 3\% in terms of accuracy and up to 11\% in terms of graphical distance over standard models on subpopulation shift benchmarks.

Image Classification

Low Precision Decentralized Distributed Training over IID and non-IID Data

1 code implementation17 Nov 2021 Sai Aparna Aketi, Sangamesh Kodge, Kaushik Roy

In this paper, we propose and show the convergence of low precision decentralized training that aims to reduce the computational complexity and communication cost of decentralized training.


L4-Norm Weight Adjustments for Converted Spiking Neural Networks

no code implementations17 Nov 2021 Jason Allred, Kaushik Roy

Converted SNNs function sufficiently well because the mean pre-firing membrane potential of a spiking neuron is proportional to the dot product of the input rate vector and the neuron weight vector, similar to the functionality of a non-spiking network.

BERMo: What can BERT learn from ELMo?

no code implementations18 Oct 2021 Sangamesh Kodge, Kaushik Roy

Experiments on the probing task from SentEval dataset show that our model performs up to $4. 65\%$ better in accuracy than the baseline with an average improvement of $2. 67\%$ on the semantic tasks.

One Timestep is All You Need: Training Spiking Neural Networks with Ultra Low Latency

1 code implementation1 Oct 2021 Sayeed Shafayet Chowdhury, Nitin Rathi, Kaushik Roy

We achieve top-1 accuracy of 93. 05%, 70. 15% and 67. 71% on CIFAR-10, CIFAR-100 and ImageNet, respectively using VGG16, with just 1 timestep.

On the Noise Stability and Robustness of Adversarially Trained Networks on NVM Crossbars

no code implementations19 Sep 2021 Deboleena Roy, Chun Tao, 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 than vanilla networks.

Oscillatory Fourier Neural Network: A Compact and Efficient Architecture for Sequential Processing

no code implementations14 Sep 2021 Bing Han, Cheng Wang, Kaushik Roy

To address these challenges, we propose a novel neuron model that has cosine activation with a time varying component for sequential processing.

Sentiment Analysis

Complexity-aware Adaptive Training and Inference for Edge-Cloud Distributed AI Systems

no code implementations14 Sep 2021 Yinghan Long, Indranil Chakraborty, Gopalakrishnan Srinivasan, Kaushik Roy

Only data with high probabilities of belonging to hard classes would be sent to the extension block for prediction.

Saliency Guided Experience Packing for Replay in Continual Learning

no code implementations10 Sep 2021 Gobinda Saha, Kaushik Roy

One way to enable such learning is to store past experiences in the form of input examples in episodic memory and replay them when learning new tasks.

Continual Learning Image Classification

Spiking Neural Networks with Improved Inherent Recurrence Dynamics for Sequential Learning

no code implementations4 Sep 2021 Wachirawit Ponghiran, Kaushik Roy

We show that SNNs can be trained for sequential tasks and propose modifications to a network of LIF neurons that enable internal states to learn long sequences and make their inherent recurrence resilient to the vanishing gradient problem.

Knowledge-intensive Language Understanding for Explainable AI

no code implementations2 Aug 2021 Amit Sheth, Manas Gaur, Kaushik Roy, Keyur Faldu

To understand and validate an AI system's outcomes (such as classification, recommendations, predictions), that lead to developing trust in the AI system, it is necessary to involve explicit domain knowledge that humans understand and use.

Decision Making Fairness

Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits

no code implementations25 Jun 2021 Kaushik Roy, Qi Zhang, Manas Gaur, Amit Sheth

Contextual Bandits find important use cases in various real-life scenarios such as online advertising, recommendation systems, healthcare, etc.

Multi-Armed Bandits Recommendation Systems

NAX: Co-Designing Neural Network and Hardware Architecture for Memristive Xbar based Computing Systems

no code implementations23 Jun 2021 Shubham Negi, Indranil Chakraborty, Aayush Ankit, Kaushik Roy

The hardware efficiency (energy, latency and area) as well as application accuracy (considering device and circuit non-idealities) of DNNs mapped to such hardware are co-dependent on network parameters, such as kernel size, depth etc.

Neural Architecture Search

NLP is Not enough -- Contextualization of User Input in Chatbots

no code implementations13 May 2021 Nathan Dolbir, Triyasha Dastidar, Kaushik Roy

AI chatbots have made vast strides in technology improvement in recent years and are already operational in many industries.

Chatbot Natural Language Processing

"Who can help me?": Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit

no code implementations12 May 2021 Manas Gaur, Kaushik Roy, Aditya Sharma, Biplav Srivastava, Amit Sheth

During the ongoing COVID-19 crisis, subreddits on Reddit, such as r/Coronavirus saw a rapid growth in user's requests for help (support seekers - SSs) including individuals with varying professions and experiences with diverse perspectives on care (support providers - SPs).

Natural Language Inference

Spatio-Temporal Pruning and Quantization for Low-latency Spiking Neural Networks

no code implementations26 Apr 2021 Sayeed Shafayet Chowdhury, Isha Garg, Kaushik Roy

Moreover, they require 8-14X lesser compute energy compared to their unpruned standard deep learning counterparts.

Model Compression Quantization

Fusion-FlowNet: Energy-Efficient Optical Flow Estimation using Sensor Fusion and Deep Fused Spiking-Analog Network Architectures

no code implementations19 Mar 2021 Chankyu Lee, Adarsh Kumar Kosta, Kaushik Roy

Standard frame-based cameras that sample light intensity frames are heavily impacted by motion blur for high-speed motion and fail to perceive scene accurately when the dynamic range is high.

Optical Flow Estimation

Gradient Projection Memory for Continual Learning

1 code implementation ICLR 2021 Gobinda Saha, Isha Garg, Kaushik Roy

The ability to learn continually without forgetting the past tasks is a desired attribute for artificial learning systems.

Continual Learning Image Classification

Knowledge Infused Policy Gradients for Adaptive Pandemic Control

no code implementations11 Feb 2021 Kaushik Roy, Qi Zhang, Manas Gaur, Amit Sheth

To this end, we introduce a mathematical framework for KIPG methods that can (a) induce relevant feature counts over multi-relational features of the world, (b) handle latent non-homogeneous counts as hidden variables that are linear combinations of kernelized aggregates over the features, and (b) infuse knowledge as functional constraints in a principled manner.

Decision Making

"Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision

no code implementations1 Feb 2021 Kaushik Roy, Usha Lokala, Vedant Khandelwal, Amit Sheth

With strong marketing advocacy of the benefits of cannabis use for improved mental health, cannabis legalization is a priority among legislators.

Contrastive Learning Relation Extraction

DCT-SNN: Using DCT To Distribute Spatial Information Over Time for Low-Latency Spiking Neural Networks

no code implementations ICCV 2021 Isha Garg, Sayeed Shafayet Chowdhury, Kaushik Roy

Notably, DCT-SNN performs inference with 2-14X reduced latency compared to other state-of-the-art SNNs, while achieving comparable accuracy to their standard deep learning counterparts.

DIET-SNN: A Low-Latency Spiking Neural Network with Direct Input Encoding & Leakage and Threshold Optimization

no code implementations1 Jan 2021 Nitin Rathi, Kaushik Roy

The trained membrane leak controls the flow of input information and attenuates irrelevant inputs to increase the activation sparsity in the convolutional and linear layers of the network.

Image Classification

DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural Networks

1 code implementation5 Oct 2020 Isha Garg, Sayeed Shafayet Chowdhury, Kaushik Roy

Notably, DCT-SNN performs inference with 2-14X reduced latency compared to other state-of-the-art SNNs, while achieving comparable accuracy to their standard deep learning counterparts.

On the Intrinsic Robustness of NVM Crossbars Against Adversarial Attacks

no code implementations27 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.

Image Generation

DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks

no code implementations9 Aug 2020 Nitin Rathi, Kaushik Roy

The trained membrane leak controls the flow of input information and attenuates irrelevant inputs to increase the activation sparsity in the convolutional and dense layers of the network.

Image Classification

TREND: Transferability based Robust ENsemble Design

1 code implementation4 Aug 2020 Deepak Ravikumar, Sangamesh Kodge, Isha Garg, Kaushik Roy

In this work, we study the effect of network architecture, initialization, optimizer, input, weight and activation quantization on transferability of adversarial samples.

Adversarial Robustness Quantization

Towards Understanding the Effect of Leak in Spiking Neural Networks

no code implementations15 Jun 2020 Sayeed Shafayet Chowdhury, Chankyu Lee, Kaushik Roy

While the leaky models have been argued as more bioplausible, a comparative analysis between models with and without leak from a purely computational point of view demands attention.

Fitted Q-Learning for Relational Domains

no code implementations10 Jun 2020 Srijita Das, Sriraam Natarajan, Kaushik Roy, Ronald Parr, Kristian Kersting

We consider the problem of Approximate Dynamic Programming in relational domains.


Conditionally Deep Hybrid Neural Networks Across Edge and Cloud

no code implementations21 May 2020 Yinghan Long, Indranil Chakraborty, Kaushik Roy

The proposed network can be deployed in a distributed manner, consisting of quantized layers and early exits at the edge and full-precision layers on the cloud.

Classification General Classification +2

Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation

1 code implementation ICLR 2020 Nitin Rathi, Gopalakrishnan Srinivasan, Priyadarshini Panda, Kaushik Roy

We propose a hybrid training methodology: 1) take a converted SNN and use its weights and thresholds as an initialization step for spike-based backpropagation, and 2) perform incremental spike-timing dependent backpropagation (STDB) on this carefully initialized network to obtain an SNN that converges within few epochs and requires fewer time steps for input processing.

Image Classification

Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment

no code implementations21 Apr 2020 Maryam Parsa, Catherine D. Schuman, Prasanna Date, Derek C. Rose, Bill Kay, J. Parker Mitchell, Steven R. Young, Ryan Dellana, William Severa, Thomas E. Potok, Kaushik Roy

In this work, we introduce a Bayesian approach for optimizing the hyperparameters of an algorithm for training binary communication networks that can be deployed to neuromorphic hardware.

Hyperparameter Optimization

IMAC: In-memory multi-bit Multiplication andACcumulation in 6T SRAM Array

no code implementations27 Mar 2020 Mustafa Ali, Akhilesh Jaiswal, Sangamesh Kodge, Amogh Agrawal, Indranil Chakraborty, Kaushik Roy

`In-memory computing' is being widely explored as a novel computing paradigm to mitigate the well known memory bottleneck.

Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations

1 code implementation ECCV 2020 Saima Sharmin, Nitin Rathi, Priyadarshini Panda, Kaushik Roy

Our results suggest that SNNs trained with LIF neurons and smaller number of timesteps are more robust than the ones with IF (Integrate-Fire) neurons and larger number of timesteps.

Adversarial Robustness

Vec2Face: Unveil Human Faces from their Blackbox Features in Face Recognition

no code implementations CVPR 2020 Chi Nhan Duong, Thanh-Dat Truong, Kha Gia Quach, Hung Bui, Kaushik Roy, Khoa Luu

Unveiling face images of a subject given his/her high-level representations extracted from a blackbox Face Recognition engine is extremely challenging.

Face Recognition Image Reconstruction +1

GENIEx: A Generalized Approach to Emulating Non-Ideality in Memristive Xbars using Neural Networks

no code implementations15 Mar 2020 Indranil Chakraborty, Mustafa Fayez Ali, Dong Eun Kim, Aayush Ankit, Kaushik Roy

Further, using the functional simulator and GENIEx, we demonstrate that an analytical model can overestimate the degradation in classification accuracy by $\ge 10\%$ on CIFAR-100 and $3. 7\%$ on ImageNet datasets compared to GENIEx.

Emerging Technologies

Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks

2 code implementations ECCV 2020 Chankyu Lee, Adarsh Kumar Kosta, Alex Zihao Zhu, Kenneth Chaney, Kostas Daniilidis, Kaushik Roy

Spiking Neural Networks (SNNs) serve as ideal paradigms to handle event camera outputs, but deep SNNs suffer in terms of performance due to the spike vanishing phenomenon.

Computer Vision Motion Detection +2

Explicitly Trained Spiking Sparsity in Spiking Neural Networks with Backpropagation

no code implementations2 Mar 2020 Jason M. Allred, Steven J. Spencer, Gopalakrishnan Srinivasan, Kaushik Roy

Spiking Neural Networks (SNNs) are being explored for their potential energy efficiency resulting from sparse, event-driven computations.

Relevant-features based Auxiliary Cells for Energy Efficient Detection of Natural Errors

no code implementations25 Feb 2020 Sai Aparna Aketi, Priyadarshini Panda, Kaushik Roy

To address this issue, we propose an ensemble of classifiers at hidden layers to enable energy efficient detection of natural errors.

Classification General Classification +1

RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network

1 code implementation CVPR 2020 Bing Han, Gopalakrishnan Srinivasan, Kaushik Roy

We find that performance degradation in the converted SNN stems from using "hard reset" spiking neuron that is driven to fixed reset potential once its membrane potential exceeds the firing threshold, leading to information loss during SNN inference.

Gradual Channel Pruning while Training using Feature Relevance Scores for Convolutional Neural Networks

1 code implementation23 Feb 2020 Sai Aparna Aketi, Sourjya Roy, Anand Raghunathan, Kaushik Roy

To address all the above issues, we present a simple-yet-effective gradual channel pruning while training methodology using a novel data-driven metric referred to as feature relevance score.

Model Compression

SPACE: Structured Compression and Sharing of Representational Space for Continual Learning

1 code implementation23 Jan 2020 Gobinda Saha, Isha Garg, Aayush Ankit, Kaushik Roy

A minimal number of extra dimensions required to explain the current task are added to the Core space and the remaining Residual is freed up for learning the next task.

Continual Learning

X-CHANGR: Changing Memristive Crossbar Mapping for Mitigating Line-Resistance Induced Accuracy Degradation in Deep Neural Networks

no code implementations29 Jun 2019 Amogh Agrawal, Chankyu Lee, Kaushik Roy

We rank the DNN weights and kernels based on a sensitivity analysis, and re-arrange the columns such that the most sensitive kernels are mapped closer to the drivers, thereby minimizing the impact of errors on the overall accuracy.

Emerging Technologies

PABO: Pseudo Agent-Based Multi-Objective Bayesian Hyperparameter Optimization for Efficient Neural Accelerator Design

no code implementations11 Jun 2019 Maryam Parsa, Aayush Ankit, Amirkoushyar Ziabari, Kaushik Roy

The ever increasing computational cost of Deep Neural Networks (DNN) and the demand for energy efficient hardware for DNN acceleration has made accuracy and hardware cost co-optimization for DNNs tremendously important, especially for edge devices.

Hyperparameter Optimization

Reinforcement Learning with Low-Complexity Liquid State Machines

1 code implementation4 Jun 2019 Wachirawit Ponghiran, Gopalakrishnan Srinivasan, Kaushik Roy

We propose reinforcement learning on simple networks consisting of random connections of spiking neurons (both recurrent and feed-forward) that can learn complex tasks with very little trainable parameters.

Atari Games Q-Learning +1

Constructing Energy-efficient Mixed-precision Neural Networks through Principal Component Analysis for Edge Intelligence

1 code implementation4 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.

Autonomous Vehicles Dimensionality Reduction +4

Synthesizing Images from Spatio-Temporal Representations using Spike-based Backpropagation

no code implementations24 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.

Image Generation

Evaluating the Stability of Recurrent Neural Models during Training with Eigenvalue Spectra Analysis

no code implementations8 May 2019 Priyadarshini Panda, Efstathia Soufleri, Kaushik Roy

We analyze the stability of recurrent networks, specifically, reservoir computing models during training by evaluating the eigenvalue spectra of the reservoir dynamics.

A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networks

no code implementations7 May 2019 Saima Sharmin, Priyadarshini Panda, Syed Shakib Sarwar, Chankyu Lee, Wachirawit Ponghiran, Kaushik Roy

In this work, we present, for the first time, a comprehensive analysis of the behavior of more bio-plausible networks, namely Spiking Neural Network (SNN) under state-of-the-art adversarial tests.

Adversarial Robustness

ReStoCNet: Residual Stochastic Binary Convolutional Spiking Neural Network for Memory-Efficient Neuromorphic Computing

no code implementations11 Feb 2019 Gopalakrishnan Srinivasan, Kaushik Roy

In addition, we introduce residual connections between the stacked convolutional layers to improve the hierarchical feature learning capability of deep SNNs.

Dimensionality Reduction

Discretization based Solutions for Secure Machine Learning against Adversarial Attacks

no code implementations8 Feb 2019 Priyadarshini Panda, Indranil Chakraborty, Kaushik Roy

Specifically, discretizing the input space (or allowed pixel levels from 256 values or 8-bit to 4 values or 2-bit) extensively improves the adversarial robustness of DLNs for a substantial range of perturbations for minimal loss in test accuracy.

Adversarial Robustness

Controlled Forgetting: Targeted Stimulation and Dopaminergic Plasticity Modulation for Unsupervised Lifelong Learning in Spiking Neural Networks

no code implementations8 Feb 2019 Jason M. Allred, Kaushik Roy

Stochastic gradient descent requires that training samples be drawn from a uniformly random distribution of the data.

Efficient Hybrid Network Architectures for Extremely Quantized Neural Networks Enabling Intelligence at the Edge

no code implementations1 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.

Edge-computing Quantization

A Low Effort Approach to Structured CNN Design Using PCA

no code implementations15 Dec 2018 Isha Garg, Priyadarshini Panda, Kaushik Roy

We demonstrate the proposed methodology on AlexNet and VGG style networks on the CIFAR-10, CIFAR-100 and ImageNet datasets, and successfully achieve an optimized architecture with a reduction of up to 3. 8X and 9X in the number of operations and parameters respectively, while trading off less than 1% accuracy.

Dimensionality Reduction Model Compression

Non-Volume Preserving-based Fusion to Group-Level Emotion Recognition on Crowd Videos

no code implementations28 Nov 2018 Kha Gia Quach, Ngan Le, Chi Nhan Duong, Ibsa Jalata, Kaushik Roy, Khoa Luu

To demonstrate the robustness and effectiveness of each component in the proposed approach, three experiments were conducted: (i) evaluation on AffectNet database to benchmark the proposed EmoNet for recognizing facial expression; (ii) evaluation on EmotiW2018 to benchmark the proposed deep feature level fusion mechanism NVPF; and, (iii) examine the proposed TNVPF on an innovative Group-level Emotion on Crowd Videos (GECV) dataset composed of 627 videos collected from publicly available sources.

Emotion Recognition

RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars

no code implementations31 Aug 2018 Shubham Jain, Abhronil Sengupta, Kaushik Roy, Anand Raghunathan

We present RxNN, a fast and accurate simulation framework to evaluate large-scale DNNs on resistive crossbar systems.

Implicit Generative Modeling of Random Noise during Training for Adversarial Robustness

1 code implementation5 Jul 2018 Priyadarshini Panda, Kaushik Roy

We introduce a Noise-based prior Learning (NoL) approach for training neural networks that are intrinsically robust to adversarial attacks.

Adversarial Robustness

Xcel-RAM: Accelerating Binary Neural Networks in High-Throughput SRAM Compute Arrays

no code implementations1 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

Exploiting Inherent Error-Resiliency of Neuromorphic Computing to achieve Extreme Energy-Efficiency through Mixed-Signal Neurons

no code implementations13 Jun 2018 Baibhab Chatterjee, Priyadarshini Panda, Shovan Maity, Ayan Biswas, Kaushik Roy, Shreyas Sen

In this work, we will analyze, compare and contrast existing neuron architectures with a proposed mixed-signal neuron (MS-N) in terms of performance, power and noise, thereby demonstrating the applicability of the proposed mixed-signal neuron for achieving extreme energy-efficiency in neuromorphic computing.

General Classification

Tree-CNN: A Hierarchical Deep Convolutional Neural Network for Incremental Learning

1 code implementation15 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.

Computer Vision Incremental Learning +1

Going Deeper in Spiking Neural Networks: VGG and Residual Architectures

no code implementations7 Feb 2018 Abhronil Sengupta, Yuting Ye, Robert Wang, Chiao Liu, Kaushik Roy

Over the past few years, Spiking Neural Networks (SNNs) have become popular as a possible pathway to enable low-power event-driven neuromorphic hardware.

Chaos-guided Input Structuring for Improved Learning in Recurrent Neural Networks

no code implementations26 Dec 2017 Priyadarshini Panda, Kaushik Roy

Anatomical studies demonstrate that brain reformats input information to generate reliable responses for performing computations.

Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing

no code implementations7 Dec 2017 Syed Shakib Sarwar, Aayush Ankit, Kaushik Roy

We propose an efficient training methodology and incrementally growing DCNN to learn new tasks while sharing part of the base network.

General Classification Image Classification +2

An Energy-Efficient Mixed-Signal Neuron for Inherently Error-Resilient Neuromorphic Systems

no code implementations24 Oct 2017 Baibhab Chatterjee, Priyadarshini Panda, Shovan Maity, Kaushik Roy, Shreyas Sen

This work presents the design and analysis of a mixed-signal neuron (MS-N) for convolutional neural networks (CNN) and compares its performance with a digital neuron (Dig-N) in terms of operating frequency, power and noise.

STDP Based Pruning of Connections and Weight Quantization in Spiking Neural Networks for Energy Efficient Recognition

no code implementations12 Oct 2017 Nitin Rathi, Priyadarshini Panda, Kaushik Roy

We present a sparse SNN topology where non-critical connections are pruned to reduce the network size and the remaining critical synapses are weight quantized to accommodate for limited conductance levels.

General Classification Quantization

TraNNsformer: Neural network transformation for memristive crossbar based neuromorphic system design

no code implementations26 Aug 2017 Aayush Ankit, Abhronil Sengupta, Kaushik Roy

Implementation of Neuromorphic Systems using post Complementary Metal-Oxide-Semiconductor (CMOS) technology based Memristive Crossbar Array (MCA) has emerged as a promising solution to enable low-power acceleration of neural networks.

Network Pruning

Gabor Filter Assisted Energy Efficient Fast Learning Convolutional Neural Networks

no code implementations12 May 2017 Syed Shakib Sarwar, Priyadarshini Panda, Kaushik Roy

This combination creates a balanced system that gives better training performance in terms of energy and time, compared to the standalone CNN (without any Gabor kernels), in exchange for tolerable accuracy degradation.

Computer Vision Face Detection +1

ASP: Learning to Forget with Adaptive Synaptic Plasticity in Spiking Neural Networks

no code implementations22 Mar 2017 Priyadarshini Panda, Jason M. Allred, Shriram Ramanathan, Kaushik Roy

Against this backdrop, we present a novel unsupervised learning mechanism ASP (Adaptive Synaptic Plasticity) for improved recognition with Spiking Neural Networks (SNNs) for real time on-line learning in a dynamic environment.


Convolutional Spike Timing Dependent Plasticity based Feature Learning in Spiking Neural Networks

no code implementations10 Mar 2017 Priyadarshini Panda, Gopalakrishnan Srinivasan, Kaushik Roy

Brain-inspired learning models attempt to mimic the cortical architecture and computations performed in the neurons and synapses constituting the human brain to achieve its efficiency in cognitive tasks.

Object Recognition

RESPARC: A Reconfigurable and Energy-Efficient Architecture with Memristive Crossbars for Deep Spiking Neural Networks

no code implementations20 Feb 2017 Aayush Ankit, Abhronil Sengupta, Priyadarshini Panda, Kaushik Roy

In this paper, we propose RESPARC - a reconfigurable and energy efficient architecture built-on Memristive Crossbar Arrays (MCA) for deep Spiking Neural Networks (SNNs).

Proposal for a Leaky-Integrate-Fire Spiking Neuron based on Magneto-Electric Switching of Ferro-magnets

1 code implementation29 Sep 2016 Akhilesh Jaiswal, Sourjya Roy, Gopalakrishnan Srinivasan, Kaushik Roy

The efficiency of the human brain in performing classification tasks has attracted considerable research interest in brain-inspired neuromorphic computing.

FALCON: Feature Driven Selective Classification for Energy-Efficient Image Recognition

no code implementations12 Sep 2016 Priyadarshini Panda, Aayush Ankit, Parami Wijesinghe, Kaushik Roy

We evaluate our approach for a 12-object classification task on the Caltech101 dataset and 10-object task on CIFAR-10 dataset by constructing FALCON models on the NeuE platform in 45nm technology.

Classification Computer Vision +1

Attention Tree: Learning Hierarchies of Visual Features for Large-Scale Image Recognition

no code implementations1 Aug 2016 Priyadarshini Panda, Kaushik Roy

A set of binary classifiers is organized on top of the learnt hierarchy to minimize the overall test-time complexity.

Image Classification Overall - Test

Significance Driven Hybrid 8T-6T SRAM for Energy-Efficient Synaptic Storage in Artificial Neural Networks

no code implementations27 Feb 2016 Gopalakrishnan Srinivasan, Parami Wijesinghe, Syed Shakib Sarwar, Akhilesh Jaiswal, Kaushik Roy

Our analysis on a widely used digit recognition dataset indicates that the voltage can be scaled by 200mV from the nominal operating voltage (950mV) for practically no loss (less than 0. 5%) in accuracy (22nm predictive technology).

General Classification

Multiplier-less Artificial Neurons Exploiting Error Resiliency for Energy-Efficient Neural Computing

no code implementations27 Feb 2016 Syed Shakib Sarwar, Swagath Venkataramani, Anand Raghunathan, Kaushik Roy

Multipliers consume most of the processing energy in the digital neurons, and thereby in the hardware implementations of artificial neural networks.

Unsupervised Regenerative Learning of Hierarchical Features in Spiking Deep Networks for Object Recognition

no code implementations3 Feb 2016 Priyadarshini Panda, Kaushik Roy

We present a spike-based unsupervised regenerative learning scheme to train Spiking Deep Networks (SpikeCNN) for object recognition problems using biologically realistic leaky integrate-and-fire neurons.

General Classification Object Recognition

Conditional Deep Learning for Energy-Efficient and Enhanced Pattern Recognition

no code implementations29 Sep 2015 Priyadarshini Panda, Abhronil Sengupta, Kaushik Roy

Deep learning neural networks have emerged as one of the most powerful classification tools for vision related applications.

Classification General Classification

Energy-Efficient Object Detection using Semantic Decomposition

no code implementations29 Sep 2015 Priyadarshini Panda, Swagath Venkataramani, Abhronil Sengupta, Anand Raghunathan, Kaushik Roy

We propose a 2-stage hierarchical classification framework, with increasing levels of complexity, wherein the first stage is trained to recognize the broad representative semantic features relevant to the object of interest.

General Classification object-detection +1

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