15 results on '"Harikrishnan Ravichandran"'
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2. A stochastic encoder using point defects in two-dimensional materials
- Author
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Harikrishnan Ravichandran, Theresia Knobloch, Shiva Subbulakshmi Radhakrishnan, Christoph Wilhelmer, Sergei P. Stepanoff, Bernhard Stampfer, Subir Ghosh, Aaryan Oberoi, Dominic Waldhoer, Chen Chen, Joan M. Redwing, Douglas E. Wolfe, Tibor Grasser, and Saptarshi Das
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Science - Abstract
Abstract While defects are undesirable for the reliability of electronic devices, particularly in scaled microelectronics, they have proven beneficial in numerous quantum and energy-harvesting applications. However, their potential for new computational paradigms, such as neuromorphic and brain-inspired computing, remains largely untapped. In this study, we harness defects in aggressively scaled field-effect transistors based on two-dimensional semiconductors to accelerate a stochastic inference engine that offers remarkable noise resilience. We use atomistic imaging, density functional theory calculations, device modeling, and low-temperature transport experiments to offer comprehensive insight into point defects in WSe2 FETs and their impact on random telegraph noise. We then use random telegraph noise to construct a stochastic encoder and demonstrate enhanced inference accuracy for noise-inflicted medical-MNIST images compared to a deterministic encoder, utilizing a pre-trained spiking neural network. Our investigation underscores the importance of leveraging intrinsic point defects in 2D materials as opportunities for neuromorphic computing.
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- 2024
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3. An all 2D bio-inspired gustatory circuit for mimicking physiology and psychology of feeding behavior
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Subir Ghosh, Andrew Pannone, Dipanjan Sen, Akshay Wali, Harikrishnan Ravichandran, and Saptarshi Das
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Science - Abstract
Abstract Animal behavior involves complex interactions between physiology and psychology. However, most AI systems neglect psychological factors in decision-making due to a limited understanding of the physiological-psychological connection at the neuronal level. Recent advancements in brain imaging and genetics have uncovered specific neural circuits that regulate behaviors like feeding. By developing neuro-mimetic circuits that incorporate both physiology and psychology, a new emotional-AI paradigm can be established that bridges the gap between humans and machines. This study presents a bio-inspired gustatory circuit that mimics adaptive feeding behavior in humans, considering both physiological states (hunger) and psychological states (appetite). Graphene-based chemitransistors serve as artificial gustatory taste receptors, forming an electronic tongue, while 1L-MoS2 memtransistors construct an electronic-gustatory-cortex comprising a hunger neuron, appetite neuron, and feeding circuit. This work proposes a novel paradigm for emotional neuromorphic systems with broad implications for human health. The concept of gustatory emotional intelligence can extend to other sensory systems, benefiting future humanoid AI.
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- 2023
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4. A bio-inspired visuotactile neuron for multisensory integration
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Muhtasim Ul Karim Sadaf, Najam U Sakib, Andrew Pannone, Harikrishnan Ravichandran, and Saptarshi Das
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Science - Abstract
Abstract Multisensory integration is a salient feature of the brain which enables better and faster responses in comparison to unisensory integration, especially when the unisensory cues are weak. Specialized neurons that receive convergent input from two or more sensory modalities are responsible for such multisensory integration. Solid-state devices that can emulate the response of these multisensory neurons can advance neuromorphic computing and bridge the gap between artificial and natural intelligence. Here, we introduce an artificial visuotactile neuron based on the integration of a photosensitive monolayer MoS2 memtransistor and a triboelectric tactile sensor which minutely captures the three essential features of multisensory integration, namely, super-additive response, inverse effectiveness effect, and temporal congruency. We have also realized a circuit which can encode visuotactile information into digital spiking events, with probability of spiking determined by the strength of the visual and tactile cues. We believe that our comprehensive demonstration of bio-inspired and multisensory visuotactile neuron and spike encoding circuitry will advance the field of neuromorphic computing, which has thus far primarily focused on unisensory intelligence and information processing.
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- 2023
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5. Hardware implementation of Bayesian network based on two-dimensional memtransistors
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Yikai Zheng, Harikrishnan Ravichandran, Thomas F. Schranghamer, Nicholas Trainor, Joan M. Redwing, and Saptarshi Das
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Science - Abstract
Bayesian networks are applied to resolve several types of probabilistic problems. Here, Das et al. develop a stochastic computing hardware platform using two-dimensional memtransistors for the implementation of Bayesian network with high accuracy.
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- 2022
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6. Hardware Trojans based on two-dimensional memtransistors
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Akshay Wali, Harikrishnan Ravichandran, and Saptarshi Das
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General Materials Science - Abstract
Hardware Trojans (HTs) have emerged as a major security threat for integrated circuits (ICs) owing to the involvement of untrustworthy actors in the globally distributed semiconductor supply chain.
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- 2023
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7. Logic Locking of Integrated Circuits Enabled by Nanoscale MoS2-Based Memtransistors
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Shakya Chakrabarti, Akshay Wali, Harikrishnan Ravichandran, Shamik Kundu, Thomas F. Schranghamer, Kanad Basu, and Saptarshi Das
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General Materials Science - Published
- 2022
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8. Radiation Resilient Two-Dimensional Electronics
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Thomas F. Schranghamer, Andrew Pannone, Harikrishnan Ravichandran, Sergei P. Stepanoff, Nicholas Trainor, Joan M. Redwing, Douglas E. Wolfe, and Saptarshi Das
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General Materials Science - Published
- 2023
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9. An All 2D Bio-inspired Gustatory Circuit for Mimicking Physiology and Psychology of Feeding Behavior
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Andrew Pannone, Harikrishnan Ravichandran, Akshay Wali, Dipanjan Sen, Subir Ghosh, and Saptarshi Das
- Abstract
Animal behavior is a complex interaction between physiology and psychology. Yet, most artificial intelligence (AI) systems do not take into account psychological factors in their decision-making. One obvious reason for this exclusion is the lack of comprehensive understanding of the connection between physiology and psychology at the neuronal level. However, recent advances in brain imaging and molecular and genetic tools have revealed that there are specific neural circuits in the brain through which physiology and psychology are hardwired for regulating animal behaviors such as feeding. Developing neuro-mimetic circuits that can integrate the influence of both physiology and psychology can enable a new emotional-AI paradigm that can bridge the gap between humans and machines. Here we demonstrate, for the first time, a bio-inspired gustatory circuit that can mimic adaptive feeding behavior for humans based on both the physiological states of the body such as hunger, and the psychological state of the mind such as appetite. For our demonstration, we use graphene-based chemitransistors as artificial gustatory taste receptor neurons to design an “electronic tongue” and monolayer MoS2 based memtransistors to design an “electronic gustatory cortex” that include physiology-drive “hunger neuron”, psychology-driven “appetite neuron” and a “feeding circuit”. We also show adaptive feeding behavior by exploiting the analog and non-volatile programming capability of the MoS2 memtransistors. We believe that our demonstration can institute a new paradigm for emotional neuromorphic systems and at the same time have widespread consequences for human health. The concept of gustatory emotional intelligence introduced in this work can also be translated to other sensory systems including visual, audio, tactile, and olfactory emotional intelligence to aid future humanoid AI.
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- 2023
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10. A Monolithic Stochastic Computing Architecture for Energy Efficient Arithmetic
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Harikrishnan Ravichandran, Yikai Zheng, Thomas F Schranghamer, Nicholas Trainor, Joan M. Redwing, and Saptarshi Das
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Mechanics of Materials ,Mechanical Engineering ,General Materials Science - Abstract
As the energy and hardware investments necessary for conventional high-precision digital computing continues to explode in the emerging era of artificial intelligence, deep learning, and big data, a change in paradigm that can trade precision for energy and resource efficiency is being sought for many computing applications. Stochastic computing (SC) is an attractive alternative since unlike digital computers, which require many logic gates and a high transistor volume to perform basic arithmetic operations such as addition, subtraction, multiplication, sorting, etc., SC can implement the same using simple logic gates. While it is possible to accelerate SC using traditional silicon complementary metal-oxide-semiconductor (CMOS) technology, the need for extensive hardware investment to generate stochastic bits (s-bits), the fundamental computing primitive for SC, makes it less attractive. Memristor and spin-based devices offer natural randomness but depend on hybrid designs involving CMOS peripherals for accelerating SC, which increases area and energy burden. Here, we overcome the limitations of existing and emerging technologies and experimentally demonstrate a standalone SC architecture embedded in memory based on two-dimensional (2D) memtransistors. Our monolithic and non-von Neumann SC architecture consumes a miniscule amount of energy (1 nJ) for both s-bit generation and arithmetic operations, and it also occupies a small hardware footprint, highlighting the benefits of SC. This article is protected by copyright. All rights reserved.
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- 2022
11. Hardware Acceleration of Bayesian Network based on Two-dimensional Memtransistors
- Author
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Yikai Zheng, Harikrishnan Ravichandran, Thomas Schranghamer, Nicholas Trainor, Joan Redwing, and Saptarshi Das
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Long before Bayes proposed his landmark theorem on the probability of an event, based on prior knowledge of other events, natural intelligence has adopted Bayesian inference as a tool to ensure survival for almost all species. The fact that living beings must make critical decision for finding food, avoiding predators, and locating mates, based on information gathered by their sensory organs with limited sensitivity and under noisy surroundings, emphasizes the importance of probabilistic computing for evolutionary success. While the anatomy of neural hardware that accomplishes such task is far from being known, it is clear that stochastic computing is a fundamental aspect of natural intelligence, and Bayesian networks (BNs) are powerful mathematical constructs for the same. Interestingly, BNs also find widespread application in many real-world probabilistic problems including diagnostics, forecasting, computer vision, etc. While the concept of BN is well known, there are very limited hardware realizations of BN. CMOS [1, 2] based BNs require massive hardware resources (thousands of transistors), whereas, memristor [3-5] and spintronics [6-8] based BNs necessitate hybrid design with CMOS peripherals limiting the area and energy efficiency [9]. Here, we circumvent these challenges by introducing a compact and low-power BN architecture embedded in memory based on 2D memtransistors.
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- 2022
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12. A Monolithic Stochastic Computing Architecture for Energy and Area Efficient Arithmetic
- Author
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Harikrishnan Ravichandran, Yikai Zheng, Thomas Schranghamer, Nicholas Trainor, Joan Redwing, and Saptarshi Das
- Abstract
As the energy and hardware investments necessary for conventional high-precision digital computing continues to explode in the emerging era of artificial intelligence, deep learning, and Big-data [1-4], a change in paradigm that can trade precision for energy and resource efficiency is being sought for many computing applications. Stochastic computing (SC) is an attractive alternative since unlike digital computers, which require many logic gates and a high transistor volume to perform basic arithmetic operations such as addition, subtraction, multiplication, sorting etc., SC can implement the same using simple logic gates [5, 6]. While it is possible to accelerate SC using traditional silicon complementary metal oxide semiconductor (CMOS) [7, 8] technology, the need for extensive hardware investment to generate stochastic bits (s-bit), the fundamental computing primitive for SC, makes it less attractive. Memristor [9-11] and spin-based devices [12-15] offer natural randomness but depend on hybrid designs involving CMOS peripherals for accelerating SC, which increases area and energy burden. Here we overcome the limitations of existing and emerging technologies and experimentally demonstrate a standalone SC architecture embedded in memory based on two-dimensional (2D) memtransistors. Our monolithic and non-von Neumann SC architecture consumes a miniscule amount of energy < 1 nano Joules for s-bit generation and to perform arithmetic operations and occupy small hardware footprint highlighting the benefits of SC.
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- 2022
- Full Text
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13. A Detailed Studies on the Rietveld Refinement Phase Matching, Quantum Confinement Effect and Barkhausen Effect Induced Magnetic Characteristics of CeFeO 3/CeO 2 Nanocomposites
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Harikrishnan Ravichandran, Baskaran Iruson, Sathyaseelan Balaraman, Mani Govindasamy, Senthilnathan Krishnamoorthy, and Manikandan Elayaperumal
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History ,Polymers and Plastics ,Business and International Management ,Industrial and Manufacturing Engineering - Published
- 2022
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14. A Machine Learning Attack Resilient True Random Number Generator Based on Stochastic Programming of Atomically Thin Transistors
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Saptarshi Das, Akshay Wali, and Harikrishnan Ravichandran
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Hardware security module ,business.industry ,Random number generation ,Computer science ,Transistor ,General Engineering ,Mobile computing ,General Physics and Astronomy ,Stochastic programming ,law.invention ,law ,Embedded system ,Component (UML) ,General Materials Science ,Internet of Things ,business - Abstract
A true random number generator (TRNG) is a critical hardware component that has become increasingly important in the era of Internet of Things (IoT) and mobile computing for ensuring secure communication and authentication schemes. While recent years have seen an upsurge in TRNGs based on nanoscale materials and devices, their resilience against machine learning (ML) attacks remains unexamined. In this article, we demonstrate a ML attack resilient, low-power, and low-cost TRNG by exploiting stochastic programmability of floating gate (FG) field effect transistors (FETs) with atomically thin channel materials. The origin of stochasticity is attributed to the probabilistic nature of charge trapping and detrapping phenomena in the FG. Our TRNG also satisfies other requirements, which include high entropy, uniformity, uniqueness, and unclonability. Furthermore, the generated bit-streams pass NIST randomness tests without any postprocessing. Our findings are important in the context of hardware security for resource constrained IoT edge devices, which are becoming increasingly vulnerable to ML attacks.
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- 2021
15. Microwave assisted synthesis and characterization of Fe3+-O-Fe3+ sublattice magnetic moment influencing ferromagnetism exhibited erbium orthoferrite sublattice (ErFeO3) perovskite nanopowders
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Baskaran Irusan, Manikandan Elayaperumal, Sathyaseelan Balaraman, Senthilnathan Krishnamoorthy, Mani Govindasamy, and Harikrishnan Ravichandran
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Orthoferrite ,Materials science ,Rietveld refinement ,Mechanical Engineering ,Metals and Alloys ,Analytical chemistry ,Crystal structure ,Bond length ,chemistry.chemical_compound ,Molecular geometry ,chemistry ,Mechanics of Materials ,Molecular vibration ,Materials Chemistry ,Orthorhombic crystal system ,Perovskite (structure) - Abstract
In this work, we report the systematic experimental synthesis and characterization of rare earth Erbium metal incorporated orthoferrite (EOF) nanoparticles (NPs) via microwave-assisted technique. The instrument micro-oven was used to induce exothermic heating inside the sample which causes the formation of ErFeO3 nano-crystallites. The unit cell structure of Erbium substituted orthoferrite nano-crystallite is studied by using experimental results and computational techniques by simulating the orthorhombic phase. Crystal lattice structure and its direct and reciprocal parameters are analysed by performing the Rietveld refinement process on the Powder X-ray diffraction pattern (P-XRD) data which confirms the fabricated unit cell structure belongs to the orthorhombic-cubic phase (spacegroup: Pbnm) with slightly induced distortion in bond distance between atoms. From FTIR fingerprint region of metal oxides, the vibrational frequency of harmonically oscillating Er-O groups in ErFeO3 unit cells was observed at (414 and 420 cm−1) and this value is consistent with the theoretical vibrational frequencies of Er-O (424.0410 and 426.5122 cm−1) calculated based on the Hooks law by considering the Er-O groups as a harmonic system. Similarly, the observed and calculated vibrational frequencies of Fe-O groups are found at (536, 570 and 576 cm−1) and (530.2621, 531.4341 and 549.7296 cm-1) respectively. The band gap of the ErFeO3 is found to be 2.1 eV. Weak Ferromagnetic property is observed in MH plot for the synthesized EOF nanoparticles due to formation of lower bond angle of Fe3+-O-Fe3+ sublattice with 140.5782° and 147.6881°. SEM image of ErFeO3 is confirmed the spherically distributed nano-crystallites with size ranging from 62 to 166 nm. In this research, Match (v3) and VESTA open-source programs are used for the Rietveld refinement process and to calculate bond distance between atoms in the unit cell.
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- 2022
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