Manan Suri
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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: Emerging non-volatile memory (NVM) technologies based nano-oscillators: Materials to applications
Emerging non-volatile memory (NVM) technologies based nano-oscillators: Materials to applications Open
This comprehensive study provides a detailed review toward ongoing research on emerging non-volatile memory technologies based nano-oscillators, i.e., from the perspective of materials to applications. Depending on the materials used to fa…
View article: Non-Invasive Qualitative Vibration Analysis using Event Camera
Non-Invasive Qualitative Vibration Analysis using Event Camera Open
This technical report investigates the application of event-based vision sensors in non-invasive qualitative vibration analysis, with a particular focus on frequency measurement and motion magnification. Event cameras, with their high temp…
View article: POEM: Performance Optimization and Endurance Management for Non-volatile Caches
POEM: Performance Optimization and Endurance Management for Non-volatile Caches Open
Non-volatile memories (NVMs), with their high storage density and ultra-low leakage power, offer promising potential for redesigning the memory hierarchy in next-generation Multi-Processor Systems-on-Chip (MPSoCs). However, the adoption of…
View article: Spike frequency adaptation: bridging neural models and neuromorphic applications
Spike frequency adaptation: bridging neural models and neuromorphic applications Open
View article: A Survey of Graph and Attention Based Hyperspectral Image Classification Methods for Remote Sensing Data
A Survey of Graph and Attention Based Hyperspectral Image Classification Methods for Remote Sensing Data Open
The use of Deep Learning techniques for classification in Hyperspectral Imaging (HSI) is rapidly growing and achieving improved performances. Due to the nature of the data captured by sensors that produce HSI images, a common issue is the …
View article: Optimized Implementation of Neuromorphic HATS Algorithm on FPGA
Optimized Implementation of Neuromorphic HATS Algorithm on FPGA Open
In this paper, we present first-ever optimized hardware implementation of a state-of-the-art neuromorphic approach Histogram of Averaged Time Surfaces (HATS) algorithm to event-based object classification in FPGA for asynchronous time-base…
View article: A survey and perspective on neuromorphic continual learning systems
A survey and perspective on neuromorphic continual learning systems Open
With the advent of low-power neuromorphic computing systems, new possibilities have emerged for deployment in various sectors, like healthcare and transport, that require intelligent autonomous applications. These applications require reli…
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: Exploiting switching properties of non-volatile memory chips for data security applications
Exploiting switching properties of non-volatile memory chips for data security applications Open
This paper presents a technique of utilizing Commercial-Off-The-Self (COTS) Non-Volatile Memory (NVM) chips for data security applications. In particular, True Random Numbers (TRNs) are generated by harnessing the latency variability obser…
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: Corrections to “Experimental Study of Adversarial Magnetic Field Exposure Attacks on Toggle MRAM Chips” [Mar 22 1480-1485]
Corrections to “Experimental Study of Adversarial Magnetic Field Exposure Attacks on Toggle MRAM Chips” [Mar 22 1480-1485] Open
In the above article [1] , the magnetic field strength mentioned was "700 KGauss." The occurrence of "K" in the units is an inadvertent, unintentional typographical error. The correct value must be "700 Gauss" in the last sentence of Secti…
View article: Single Cycle XOR (SCXOR) and Stateful n-bit Parallel Adder Implementation Using 2D RRAM Crossbar
Single Cycle XOR (SCXOR) and Stateful n-bit Parallel Adder Implementation Using 2D RRAM Crossbar Open
The motivation to find a solution to the Memory Wall problem led the research community to explore non-von-Neumann architectures. Compute In-Memory (CIM) architectures with emerging memory technologies are promising for minimizing data mov…
View article: Exploiting Nanoelectronic Properties of Memory Chips for Prevention of IC Counterfeiting
Exploiting Nanoelectronic Properties of Memory Chips for Prevention of IC Counterfeiting Open
This study presents a methodology for anticounterfeiting of Non-Volatile Memory (NVM) chips. In particular, we experimentally demonstrate a generalized methodology for detecting (i) Integrated Circuit (IC) origin, (ii) recycled or used NVM…
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: Low Power Neuromorphic EMG Gesture Classification
Low Power Neuromorphic EMG Gesture Classification Open
EMG (Electromyograph) signal based gesture recognition can prove vital for applications such as smart wearables and bio-medical neuro-prosthetic control. Spiking Neural Networks (SNNs) are promising for low-power, real-time EMG gesture rec…
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: An adaptive threshold neuron for recurrent spiking neural networks with nanodevice hardware implementation
An adaptive threshold neuron for recurrent spiking neural networks with nanodevice hardware implementation Open
We propose a Double EXponential Adaptive Threshold (DEXAT) neuron model that improves the performance of neuromorphic Recurrent Spiking Neural Networks (RSNNs) by providing faster convergence, higher accuracy and a flexible long short-term…
View article: Bird-Area Water-Bodies Dataset (BAWD) and Predictive AI Model for Avian\n Botulism Outbreak (AVI-BoT)
Bird-Area Water-Bodies Dataset (BAWD) and Predictive AI Model for Avian\n Botulism Outbreak (AVI-BoT) Open
Avian botulism is a paralytic bacterial disease in birds often leading to\nhigh fatality. In-vitro diagnostic techniques such as Mouse Bioassay, ELISA,\nPCR are usually non-preventive, post-mortem in nature, and require invasive\nsample co…
View article: Bird-Area Water-Bodies Dataset (BAWD) and Predictive AI Model for Avian Botulism Outbreak (AVI-BoT)
Bird-Area Water-Bodies Dataset (BAWD) and Predictive AI Model for Avian Botulism Outbreak (AVI-BoT) Open
Avian botulism is a paralytic bacterial disease in birds often leading to high fatality. In-vitro diagnostic techniques such as Mouse Bioassay, ELISA, PCR are usually non-preventive, post-mortem in nature, and require invasive sample colle…
View article: NV-Fogstore : Device-aware hybrid caching in fog computing environments
NV-Fogstore : Device-aware hybrid caching in fog computing environments Open
Edge caching via the placement of distributed storages throughout the network is a promising solution to reduce latency and network costs of content delivery. With the advent of the upcoming 5G future, billions of F-RAN (Fog-Radio Access N…
View article: Unified Characterization Platform for Emerging NVM Technology: Neural Network Application Benchmarking using off-the-Shelf NVM Chips
Unified Characterization Platform for Emerging NVM Technology: Neural Network Application Benchmarking using off-the-Shelf NVM Chips Open
In this paper, we present a unified FPGA based electrical test-bench for characterizing different emerging NonVolatile Memory (NVM) chips. In particular, we present detailed electrical characterization and benchmarking of multiple commerci…
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: Unified Characterization Platform for Emerging NVM Technology: Neural\n Network Application Benchmarking Using off-the-shelf NVM Chips
Unified Characterization Platform for Emerging NVM Technology: Neural\n Network Application Benchmarking Using off-the-shelf NVM Chips Open
In this paper, we present a unified FPGA based electrical test-bench for\ncharacterizing different emerging NonVolatile Memory (NVM) chips. In\nparticular, we present detailed electrical characterization and benchmarking of\nmultiple comme…
View article: Hybrid CMOS-PCM temperature sensor
Hybrid CMOS-PCM temperature sensor Open
In this paper, we propose a hybrid CMOS and phase-change memory (PCM)-relaxation-oscillator based circuit for temperature-sensing applications. Unlike conventional CMOS temperature sensors based on ring- or relaxation-oscillators, the prop…