Vivek Parmar
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View article: Corrosion Assessment in Steel Reinforcement Using Piezoelectric Sensor for Service Life Prognosis of Reinforced Concrete Structures
Corrosion Assessment in Steel Reinforcement Using Piezoelectric Sensor for Service Life Prognosis of Reinforced Concrete Structures Open
The deterioration of reinforced concrete (RC) structures due to the corrosion of steel reinforcement poses significant challenges to structural integrity and service life. This study investigates the application of electromechanical impeda…
View article: x-RAGE: eXtended Reality -- Action & Gesture Events Dataset
x-RAGE: eXtended Reality -- Action & Gesture Events Dataset Open
With the emergence of the Metaverse and focus on wearable devices in the recent years gesture based human-computer interaction has gained significance. To enable gesture recognition for VR/AR headsets and glasses several datasets focusing …
View article: Analysis of VMM computation strategies to implement BNN applications on RRAM arrays
Analysis of VMM computation strategies to implement BNN applications on RRAM arrays Open
The growing interest in edge-AI solutions and advances in the field of quantized neural networks have led to hardware efficient binary neural networks (BNNs). Extreme BNNs utilize only binary weights and activations, making them more memor…
View article: Demonstration of Differential Mode Ferroelectric Field‐Effect Transistor Array‐Based in‐Memory Computing Macro for Realizing Multiprecision Mixed‐Signal Artificial Intelligence Accelerator
Demonstration of Differential Mode Ferroelectric Field‐Effect Transistor Array‐Based in‐Memory Computing Macro for Realizing Multiprecision Mixed‐Signal Artificial Intelligence Accelerator Open
Harnessing multibit precision in nonvolatile memory (NVM)‐based synaptic core can accelerate multiply and accumulate (MAC) operation of deep neural network (DNN). However, NVM‐based synaptic cores suffer from the trade‐off between bit dens…
View article: Low-Power Hardware-Based Deep-Learning Diagnostics Support Case Study
Low-Power Hardware-Based Deep-Learning Diagnostics Support Case Study Open
Deep learning research has generated widespread interest leading to emergence of a large variety of technological innovations and applications. As significant proportion of deep learning research focuses on vision based applications, there…
View article: Fully-Binarized, Parallel, RRAM-based Computing Primitive for In-Memory Similarity Search
Fully-Binarized, Parallel, RRAM-based Computing Primitive for In-Memory Similarity Search Open
In this work, we propose a fully-binarized XOR-based IMSS (In-Memory Similarity Search) using RRAM (Resistive Random Access Memory) arrays. XOR (Exclusive OR) operation is realized using 2T-2R bitcells arranged along the column in an array…
View article: Memory-Oriented Design-Space Exploration of Edge-AI Hardware for XR Applications
Memory-Oriented Design-Space Exploration of Edge-AI Hardware for XR Applications Open
Low-Power Edge-AI capabilities are essential for on-device extended reality (XR) applications to support the vision of Metaverse. In this work, we investigate two representative XR workloads: (i) Hand detection and (ii) Eye segmentation, f…
View article: In-Memory Computation Based Mapping of Keccak-f Hash Function
In-Memory Computation Based Mapping of Keccak-f Hash Function Open
Cryptographic hash functions play a central role in data security for applications such as message authentication, data verification, and detecting malicious or illegal modification of data. However, such functions typically require intens…
View article: Hardware-Efficient Stochastic Binary CNN Architectures for Near-Sensor Computing
Hardware-Efficient Stochastic Binary CNN Architectures for Near-Sensor Computing Open
With recent advances in the field of artificial intelligence (AI) such as binarized neural networks (BNNs), a wide variety of vision applications with energy-optimized implementations have become possible at the edge. Such networks have th…
View article: Time-Multiplexed In-Memory Computation Scheme for Mapping Quantized Neural Networks on Hybrid CMOS-OxRAM Building Blocks
Time-Multiplexed In-Memory Computation Scheme for Mapping Quantized Neural Networks on Hybrid CMOS-OxRAM Building Blocks Open
In this work, we experimentally demonstrate two key building blocks for\nrealizing Binary/Ternary Neural Networks (BNNs/TNNs): (i) 130 nm CMOS based\nsigmoidal neurons and (ii) HfOx based multi-level (MLC) OxRAM-synaptic blocks.\nAn optimi…
View article: Methodology for Realizing VMM with Binary RRAM Arrays: Experimental Demonstration of Binarized-ADALINE using OxRAM Crossbar
Methodology for Realizing VMM with Binary RRAM Arrays: Experimental Demonstration of Binarized-ADALINE using OxRAM Crossbar Open
In this paper, we present an efficient hardware mapping methodology for realizing vector matrix multiplication (VMM) on resistive memory (RRAM) arrays. Using the proposed VMM computation technique, we experimentally demonstrate a binarized…
View article: Exploration of Optimized Semantic Segmentation Architectures for edge-Deployment on Drones
Exploration of Optimized Semantic Segmentation Architectures for edge-Deployment on Drones Open
In this paper, we present an analysis on the impact of network parameters for semantic segmentation architectures in context of UAV data processing. We present the analysis on the DroneDeploy Segmentation benchmark. Based on the comparativ…
View article: SLIM: Simultaneous Logic-in-Memory Computing Exploiting Bilayer Analog OxRAM Devices
SLIM: Simultaneous Logic-in-Memory Computing Exploiting Bilayer Analog OxRAM Devices Open
View article: Contrasting Advantages of Learning With Random Weights and Backpropagation in Non-Volatile Memory Neural Networks
Contrasting Advantages of Learning With Random Weights and Backpropagation in Non-Volatile Memory Neural Networks Open
Recently, a Cambrian explosion of novel, non-volatile memory (NVM) devices known as memristive devices have inspired effort in building hardware neural networks that learn like the brain. Early experimental prototypes built simple perceptr…
View article: MASTISK
MASTISK Open
In this paper, we present MASTISK (MAchine-learning and Synaptic-plasticity Technology Integrated Simulation frameworK). MASTISK is an open-source versatile and flexible tool developed in MATLAB for design exploration of dedicated neuromor…
View article: Design Exploration of Hybrid CMOS-OxRAM Deep Generative Architectures
Design Exploration of Hybrid CMOS-OxRAM Deep Generative Architectures Open
Deep Learning and its applications have gained tremendous interest recently in both academia and industry. Restricted Boltzmann Machines (RBMs) offer a key methodology to implement deep learning paradigms. This paper presents a novel appro…