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Found 129 papers, 27 papers with code
Deep Spiking Neural Network: Energy Efficiency Through Time based Coding
The real-valued ReLU activations in ANN are encoded using the spike-times of the TSC neurons in the converted TSC-SNN.
Overview of the CLPsych 2022 Shared Task: Capturing Moments of Change in Longitudinal User Posts
We provide an overview of the CLPsych 2022 Shared Task, which focusses on the automatic identification of ‘Moments of Change’ in lon- gitudinal posts by individuals on social media and its connection with information regarding mental health .
Hi Model, generating 'nice' instead of 'good' is not as bad as generating 'rice'! Towards Context and Semantic Infused Dialogue Generation Loss Function and Evaluation Metric
These lexical-based metrics have the following key limitations: (a) word-to-word matching without semantic consideration: It assigns the same credit for failure to generate 'nice' and 'rice' for 'good'.
L3DMC: Lifelong Learning using Distillation via Mixed-Curvature Space
To address the problem, we propose a distillation strategy named L3DMC that operates on mixed-curvature spaces to preserve the already-learned knowledge by modeling and maintaining complex geometrical structures.
Subspace Distillation for Continual Learning
To mitigate forgetting prior knowledge, we propose a novel knowledge distillation technique that takes into the account the manifold structure of the latent/output space of a neural network in learning novel tasks.
Memorization Through the Lens of Curvature of Loss Function Around Samples
Neural networks are overparametrized and easily overfit the datasets they train on.
S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks
Spiking Neural Networks (SNNs) are biologically plausible models that have been identified as potentially apt for the deployment for energy-efficient intelligence at the edge, particularly for sequential learning tasks.
IERL: Interpretable Ensemble Representation Learning -- Combining CrowdSourced Knowledge and Distributed Semantic Representations
Crowdsourced and expert-curated knowledge graphs such as ConceptNet are designed to capture the meaning of words from a compact set of well-defined contexts.
Knowledge-Infused Self Attention Transformers
However, the ad-hoc nature of existing methods makes it difficult to properly analyze the effects of knowledge infusion on the many moving parts or components of a transformer.
Process Knowledge-infused Learning for Clinician-friendly Explanations
In this study, we introduce Process Knowledge-infused Learning (PK-iL), a new learning paradigm that layers clinical process knowledge structures on language model outputs, enabling clinician-friendly explanations of the underlying language model predictions.
Explainable Artificial Intelligence (XAI)
Language Modelling
Best of Both Worlds: Hybrid SNN-ANN Architecture for Event-based Optical Flow Estimation
Spiking Neural Networks (SNNs) with their asynchronous event-driven compute, show great potential for extracting the spatio-temporal features from these event streams.
Cook-Gen: Robust Generative Modeling of Cooking Actions from Recipes
In this study, we explore the use of generative AI methods to extend current food computation models, primarily involving the analysis of nutrition and ingredients, to also incorporate cooking actions (e. g., add salt, fry the meat, boil the vegetables, etc.).
Segmented Recurrent Transformer: An Efficient Sequence-to-Sequence Model
Then we propose a segmented recurrent transformer (SRformer) that combines segmented attention with recurrent attention.
FEDORA: Flying Event Dataset fOr Reactive behAvior
The ability of living organisms to perform complex high speed manoeuvers in flight with a very small number of neurons and an incredibly low failure rate highlights the efficacy of these resource-constrained biological systems.
ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance
LMs augmented with ProKnow guided method generated 89% safer questions in the depression and anxiety domain.
Knowledge Graph Guided Semantic Evaluation of Language Models For User Trust
However, they do not ascribe object and concept-level meaning and semantics to the learned stochastic patterns such as those described in knowledge graphs.
Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data
Decentralized learning enables the training of deep learning models over large distributed datasets generated at different locations, without the need for a central server.
Neurosymbolic AI - Why, What, and How
Humans interact with the environment using a combination of perception - transforming sensory inputs from their environment into symbols, and cognition - mapping symbols to knowledge about the environment for supporting abstraction, reasoning by analogy, and long-term planning.
Fairness in Visual Clustering: A Novel Transformer Clustering Approach
Promoting fairness for deep clustering models in unsupervised clustering settings to reduce demographic bias is a challenging goal.
Homogenizing Non-IID datasets via In-Distribution Knowledge Distillation for Decentralized Learning
The goal of IDKD is to homogenize the data distribution across the nodes.
Demo Alleviate: Demonstrating Artificial Intelligence Enabled Virtual Assistance for Telehealth: The Mental Health Case
After the pandemic, artificial intelligence (AI) powered support for mental health care has become increasingly important.
CoDeC: Communication-Efficient Decentralized Continual Learning
Decentralized learning allows serverless training with spatially distributed data.
X-Former: In-Memory Acceleration of Transformers
Transformers have achieved great success in a wide variety of natural language processing (NLP) tasks due to the attention mechanism, which assigns an importance score for every word relative to other words in a sequence.
Continual Learning with Scaled Gradient Projection
In neural networks, continual learning results in gradient interference among sequential tasks, leading to catastrophic forgetting of old tasks while learning new ones.
Samples With Low Loss Curvature Improve Data Efficiency
SLo-curves identifies the samples with low curvatures as being more data-efficient and trains on them with an additional regularizer that penalizes high curvature of the loss surface in their vicinity.
HALSIE: Hybrid Approach to Learning Segmentation by Simultaneously Exploiting Image and Event Modalities
Our hybrid network reaches state-of-the-art performance on real-world DDD-17, MVSEC and DSEC-Semantic datasets with up to $\sim 33\times$ higher parameter efficiency and favorable inference cost (17. 9mJ per cycle), making it suitable for resource-constrained edge applications.
Hardware/Software co-design with ADC-Less In-memory Computing Hardware for Spiking Neural Networks
Spiking Neural Networks (SNNs) are bio-plausible models that hold great potential for realizing energy-efficient implementations of sequential tasks on resource-constrained edge devices.
Ranked #7 on
Gesture Recognition
on DVS128 Gesture
Application of Explainable Machine Learning in Detecting and Classifying Ransomware Families Based on API Call Analysis
This research work utilizes the frequencies of different API calls to detect and classify ransomware families.
KSAT: Knowledge-infused Self Attention Transformer -- Integrating Multiple Domain-Specific Contexts
Domain-specific language understanding requires integrating multiple pieces of relevant contextual information.
Synthetic Dataset Generation for Privacy-Preserving Machine Learning
We evaluate our method on image classification dataset (CIFAR10) and show that our synthetic data can be used for training networks from scratch, producing reasonable classification performance.
DOTIE -- Detecting Objects through Temporal Isolation of Events using a Spiking Architecture
Our technique consists of a lightweight spiking neural architecture that is able to separate events based on the speed of the corresponding objects.
Event-based Temporally Dense Optical Flow Estimation with Sequential Neural Networks
As a result, optical flow is only evaluated at a frequency much lower than the rate data is produced by an event-based camera, leading to a temporally sparse optical flow estimation.
Neighborhood Gradient Clustering: An Efficient Decentralized Learning Method for Non-IID Data Distributions
Our experiments demonstrate that \textit{NGC} and \textit{CompNGC} outperform (by $0-6\%$) the existing SoTA decentralized learning algorithm over non-IID data with significantly less compute and memory requirements.
Adaptive-SpikeNet: Event-based Optical Flow Estimation using Spiking Neural Networks with Learnable Neuronal Dynamics
Spiking Neural Networks (SNNs), with their neuro-inspired event-driven processing can efficiently handle such asynchronous data, while neuron models such as the leaky-integrate and fire (LIF) can keep track of the quintessential timing information contained in the inputs.
RAMP-Net: A Robust Adaptive MPC for Quadrotors via Physics-informed Neural Network
On the other hand, a regular data loss is used for adapting to residual disturbances (non-parametric uncertainties), unaccounted during mathematical modelling.
Can Language Models Capture Graph Semantics? From Graphs to Language Model and Vice-Versa
Knowledge Graphs are a great resource to capture semantic knowledge in terms of entities and relationships between the entities.
Process Knowledge-Infused AI: Towards User-level Explainability, Interpretability, and Safety
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.
A Co-design view of Compute in-Memory with Non-Volatile Elements for Neural Networks
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.
Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Posts
We demonstrate the challenge of using existing datasets to train a DLM for generating FQs that adhere to clinical process knowledge.
Norm-Scaling for Out-of-Distribution Detection
Therefore, applying a single threshold for all classes is not ideal since the same similarity score represents different uncertainties for different classes.
Process Knowledge-infused Learning for Suicidality Assessment on Social Media
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
The revolution in artificial intelligence (AI) brings up an enormous storage and data processing requirement.
TOFU: Towards Obfuscated Federated Updates by Encoding Weight Updates into Gradients from Proxy Data
This is done via a central server that aggregates learning in the form of weight updates.
Encoding Hierarchical Information in Neural Networks helps in Subpopulation Shift
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.
L4-Norm Weight Adjustments for Converted Spiking Neural Networks
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.
Low Precision Decentralized Distributed Training over IID and non-IID Data
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.
BERMo: What can BERT learn from ELMo?
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
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.
Intra-class Mixup for Out-of-Distribution Detection
This reduces the separability of in-distribution data from OoD data.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
On the Noise Stability and Robustness of Adversarially Trained Networks on NVM Crossbars
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.
RAPID-RL: A Reconfigurable Architecture with Preemptive-Exits for Efficient Deep-Reinforcement Learning
To address this challenge, we propose a reconfigurable architecture with preemptive exits for efficient deep RL (RAPID-RL).
Oscillatory Fourier Neural Network: A Compact and Efficient Architecture for Sequential Processing
To address these challenges, we propose a novel neuron model that has cosine activation with a time varying component for sequential processing.
Complexity-aware Adaptive Training and Inference for Edge-Cloud Distributed AI Systems
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
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.
Spiking Neural Networks with Improved Inherent Recurrence Dynamics for Sequential Learning
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
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
Explainable Artificial Intelligence (XAI)
+1
Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits
Contextual Bandits find important use cases in various real-life scenarios such as online advertising, recommendation systems, healthcare, etc.
NAX: Co-Designing Neural Network and Hardware Architecture for Memristive Xbar based Computing Systems
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.
NLP is Not enough -- Contextualization of User Input in Chatbots
AI chatbots have made vast strides in technology improvement in recent years and are already operational in many industries.
"Who can help me?": Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit
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).
Spatio-Temporal Pruning and Quantization for Low-latency Spiking Neural Networks
Moreover, they require 8-14X lesser compute energy compared to their unpruned standard deep learning counterparts.
eDarkTrends: Harnessing Social Media Trends in Substance use disorders for Opioid Listings on Cryptomarket
However, the lack of evidence on the relationship has resulted in opioids being largely inaccessible through legal means.
Fusion-FlowNet: Energy-Efficient Optical Flow Estimation using Sensor Fusion and Deep Fused Spiking-Analog Network Architectures
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.
Gradient Projection Memory for Continual Learning
The ability to learn continually without forgetting the past tasks is a desired attribute for artificial learning systems.
Knowledge Infused Policy Gradients for Adaptive Pandemic Control
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.
"Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision
With strong marketing advocacy of the benefits of cannabis use for improved mental health, cannabis legalization is a priority among legislators.
DCT-SNN: Using DCT To Distribute Spatial Information Over Time for Low-Latency Spiking Neural Networks
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
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.
Exploring Vicinal Risk Minimization for Lightweight Out-of-Distribution Detection
We utilize mixup in two ways to implement Vicinal Risk Minimization.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural Networks
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
The increasing computational demand of Deep Learning has propelled research in special-purpose inference accelerators based on emerging non-volatile memory (NVM) technologies.
DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks
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.
TREND: Transferability based Robust ENsemble Design
In this work, we study the effect of network architecture, initialization, optimizer, input, weight and activation quantization on transferability of adversarial samples.
Towards Understanding the Effect of Leak in Spiking Neural Networks
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
We consider the problem of Approximate Dynamic Programming in relational domains.
Conditionally Deep Hybrid Neural Networks Across Edge and Cloud
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.
Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation
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.
Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment
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.
IMAC: In-memory multi-bit Multiplication andACcumulation in 6T SRAM Array
`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
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.
Vec2Face: Unveil Human Faces from their Blackbox Features in Face Recognition
Unveiling face images of a subject given his/her high-level representations extracted from a blackbox Face Recognition engine is extremely challenging.
GENIEx: A Generalized Approach to Emulating Non-Ideality in Memristive Xbars using Neural Networks
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
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.
Explicitly Trained Spiking Sparsity in Spiking Neural Networks with Backpropagation
Spiking Neural Networks (SNNs) are being explored for their potential energy efficiency resulting from sparse, event-driven computations.
RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network
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.
Relevant-features based Auxiliary Cells for Energy Efficient Detection of Natural Errors
To address this issue, we propose an ensemble of classifiers at hidden layers to enable energy efficient detection of natural errors.
Gradual Channel Pruning while Training using Feature Relevance Scores for Convolutional Neural Networks
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.
SPACE: Structured Compression and Sharing of Representational Space for Continual Learning
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.
Towards Scalable, Efficient and Accurate Deep Spiking Neural Networks with Backward Residual Connections, Stochastic Softmax and Hybridization
Spiking Neural Networks (SNNs) may offer an energy-efficient alternative for implementing deep learning applications.
X-CHANGR: Changing Memristive Crossbar Mapping for Mitigating Line-Resistance Induced Accuracy Degradation in Deep Neural Networks
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
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.
Constructing Energy-efficient Mixed-precision Neural Networks through Principal Component Analysis for Edge Intelligence
The `Internet of Things' has brought increased demand for AI-based edge computing in applications ranging from healthcare monitoring systems to autonomous vehicles.
Reinforcement Learning with Low-Complexity Liquid State Machines
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.
Synthesizing Images from Spatio-Temporal Representations using Spike-based Backpropagation
The spiking autoencoders are benchmarked on MNIST and Fashion-MNIST and achieve very low reconstruction loss, comparable to ANNs.
Evaluating the Stability of Recurrent Neural Models during Training with Eigenvalue Spectra Analysis
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
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.
Enabling Spike-based Backpropagation for Training Deep Neural Network Architectures
Spiking Neural Networks (SNNs) have recently emerged as a prominent neural computing paradigm.
ReStoCNet: Residual Stochastic Binary Convolutional Spiking Neural Network for Memory-Efficient Neuromorphic Computing
In addition, we introduce residual connections between the stacked convolutional layers to improve the hierarchical feature learning capability of deep SNNs.
Discretization based Solutions for Secure Machine Learning against Adversarial Attacks
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.
Controlled Forgetting: Targeted Stimulation and Dopaminergic Plasticity Modulation for Unsupervised Lifelong Learning in Spiking Neural Networks
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
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.
PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference
We also present the PUMA compiler which translates high-level code to PUMA ISA.
Emerging Technologies Hardware Architecture
A Low Effort Approach to Structured CNN Design Using PCA
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.
Non-Volume Preserving-based Fusion to Group-Level Emotion Recognition on Crowd Videos
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.
RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars
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
We introduce a Noise-based prior Learning (NoL) approach for training neural networks that are intrinsically robust to adversarial attacks.
Xcel-RAM: Accelerating Binary Neural Networks in High-Throughput SRAM Compute Arrays
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
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.
Tree-CNN: A Hierarchical Deep Convolutional Neural Network for Incremental Learning
Over the past decade, Deep Convolutional Neural Networks (DCNNs) have shown remarkable performance in most computer vision tasks.
Going Deeper in Spiking Neural Networks: VGG and Residual Architectures
Over the past few years, Spiking Neural Networks (SNNs) have become popular as a possible pathway to enable low-power event-driven neuromorphic hardware.
Deep learning for word-level handwritten Indic script identification
These are then used to train different CNNs to select features.
Chaos-guided Input Structuring for Improved Learning in Recurrent Neural Networks
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
We propose an efficient training methodology and incrementally growing DCNN to learn new tasks while sharing part of the base network.
An Energy-Efficient Mixed-Signal Neuron for Inherently Error-Resilient Neuromorphic Systems
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
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.
TraNNsformer: Neural network transformation for memristive crossbar based neuromorphic system design
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.
Voltage-Driven Domain-Wall Motion based Neuro-Synaptic Devices for Dynamic On-line Learning
The basic building blocks of such neuromorphic systems are neurons and synapses.
Gabor Filter Assisted Energy Efficient Fast Learning Convolutional Neural Networks
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.
ASP: Learning to Forget with Adaptive Synaptic Plasticity in Spiking Neural Networks
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
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.
RESPARC: A Reconfigurable and Energy-Efficient Architecture with Memristive Crossbars for Deep Spiking Neural Networks
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
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
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.
Attention Tree: Learning Hierarchies of Visual Features for Large-Scale Image Recognition
A set of binary classifiers is organized on top of the learnt hierarchy to minimize the overall test-time complexity.
Multiplier-less Artificial Neurons Exploiting Error Resiliency for Energy-Efficient Neural Computing
Multipliers consume most of the processing energy in the digital neurons, and thereby in the hardware implementations of artificial neural networks.
Significance Driven Hybrid 8T-6T SRAM for Energy-Efficient Synaptic Storage in Artificial Neural Networks
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).
Unsupervised Regenerative Learning of Hierarchical Features in Spiking Deep Networks for Object Recognition
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.
Conditional Deep Learning for Energy-Efficient and Enhanced Pattern Recognition
Deep learning neural networks have emerged as one of the most powerful classification tools for vision related applications.
Energy-Efficient Object Detection using Semantic Decomposition
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.
