83 citations · 94 across the 18 of their papers we have counts for
18 papers
Conformalized Multimodal Uncertainty Regression and Reasoning
Domenico Parente, Nastaran Darabi, Alex C. Stutts +2
This paper introduces a lightweight uncertainty estimator capable of predicting multimodal (disjoint) uncertainty bounds by integrating conformal prediction with a deep-learning re…
Mutual Information-calibrated Conformal Feature Fusion for Uncertainty-Aware Multimodal 3D Object Detection at the Edge
Alex C. Stutts, Danilo Erricolo, Sathya Ravi +2
In the expanding landscape of AI-enabled robotics, robust quantification of predictive uncertainties is of great importance. Three-dimensional (3D) object detection, a critical rob…
ADC/DAC-Free Analog Acceleration of Deep Neural Networks with Frequency Transformation
Nastaran Darabi, Maeesha Binte Hashem, Hongyi Pan +3
The edge processing of deep neural networks (DNNs) is becoming increasingly important due to its ability to extract valuable information directly at the data source to minimize lat…
Towards Model-Size Agnostic, Compute-Free, Memorization-based Inference of Deep Learning
Davide Giacomini, Maeesha Binte Hashem, Jeremiah Suarez +2
The rapid advancement of deep neural networks has significantly improved various tasks, such as image and speech recognition. However, as the complexity of these models increases,…
Memory-Immersed Collaborative Digitization for Area-Efficient Compute-in-Memory Deep Learning
Shamma Nasrin, Maeesha Binte Hashem, Nastaran Darabi +4
This work discusses memory-immersed collaborative digitization among compute-in-memory (CiM) arrays to minimize the area overheads of a conventional analog-to-digital converter (AD…
Lightweight, Uncertainty-Aware Conformalized Visual Odometry
Alex C. Stutts, Danilo Erricolo, Theja Tulabandhula +1
Data-driven visual odometry (VO) is a critical subroutine for autonomous edge robotics, and recent progress in the field has produced highly accurate point predictions in complex e…
Roadmap for Unconventional Computing with Nanotechnology
Giovanni Finocchio, Jean Anne C. Incorvia, Joseph S. Friedman +48
In the "Beyond Moore's Law" era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same ti…
Robust Monocular Localization of Drones by Adapting Domain Maps to Depth Prediction Inaccuracies
Priyesh Shukla, Sureshkumar S., Alex C. Stutts +3
We present a novel monocular localization framework by jointly training deep learning-based depth prediction and Bayesian filtering-based pose reasoning. The proposed cross-modal f…
MC-CIM: Compute-in-Memory with Monte-Carlo Dropouts for Bayesian Edge Intelligence
Priyesh Shukla, Shamma Nasrin, Nastaran Darabi +2
We propose MC-CIM, a compute-in-memory (CIM) framework for robust, yet low power, Bayesian edge intelligence. Deep neural networks (DNN) with deterministic weights cannot express t…
ENOS: Energy-Aware Network Operator Search for Hybrid Digital and Compute-in-Memory DNN Accelerators
Shamma Nasrin, Ahish Shylendra, Yuti Kadakia +4
This work proposes a novel Energy-Aware Network Operator Search (ENOS) approach to address the energy-accuracy trade-offs of a deep neural network (DNN) accelerator. In recent year…
Probabilistic Localization of Insect-Scale Drones on Floating-Gate Inverter Arrays
Priyesh Shukla, Ankith Muralidhar, Nick Iliev +3
We propose a novel compute-in-memory (CIM)-based ultra-low-power framework for probabilistic localization of insect-scale drones. The conventional probabilistic localization approa…
MF-Net: Compute-In-Memory SRAM for Multibit Precision Inference using Memory-immersed Data Conversion and Multiplication-free Operators
Shamma Nasrin, Diaa Badawi, Ahmet Enis Cetin +2
We propose a co-design approach for compute-in-memory inference for deep neural networks (DNN). We use multiplication-free function approximators based on ell_1 norm along with a c…
Low Latency CMOS Hardware Acceleration for Fully Connected Layers in Deep Neural Networks
Nick Iliev, Amit Ranjan Trivedi
We present a novel low latency CMOS hardware accelerator for fully connected (FC) layers in deep neural networks (DNNs). The FC accelerator, FC-ACCL, is based on 128 8x8 or 16x16 p…
Low Power Unsupervised Anomaly Detection by Non-Parametric Modeling of Sensor Statistics
Ahish Shylendra, Priyesh Shukla, Saibal Mukhopadhyay +2
This work presents AEGIS, a novel mixed-signal framework for real-time anomaly detection by examining sensor stream statistics. AEGIS utilizes Kernel Density Estimation (KDE)-based…
: Markov Chain Monte Carlo Sampling in SRAM for Fast Bayesian Inference
Priyesh Shukla, Ahish Shylendra, Theja Tulabandhula +1
This work discusses the implementation of Markov Chain Monte Carlo (MCMC) sampling from an arbitrary Gaussian mixture model (GMM) within SRAM. We show a novel architecture of SRAM…
Supported-BinaryNet: Bitcell Array-based Weight Supports for Dynamic Accuracy-Latency Trade-offs in SRAM-based Binarized Neural Network
Shamma Nasrin, Srikanth Ramakrishna, Theja Tulabandhula +1
In this work, we introduce bitcell array-based support parameters to improve the prediction accuracy of SRAM-based binarized neural network (SRAM-BNN). Our approach enhances the tr…
Using your ADC and Backend Capacitors to Authenticate for Free: (Virtually) Free from Database, Enrollment, and Excessive Area/Power Overhead
Ahish Shylendra, Swarup Bhunia, Amit Ranjan Trivedi
Detection of counterfeit chips has emerged as a crucial concern. Physically-unclonable-function (PUF)-based techniques are widely used for authentication, however, require dedicate…
Straintronic magneto-tunneling-junction based ternary content addressable memory
S. Dey Manasi, M. M. Al Rashid, J. Atulasimha +2
Straintronic magneto-tunneling junction (s-MTJ) switches, whose resistances are controlled with voltage-generated strain in the magnetostrictive free layer of the MTJ, are extremel…