Výsledky vyhledávání - "Rajiv V. Joshi"

Akademický článek

Reliable and Energy-Efficient Diabetic Retinopathy Screening Using Memristor-Based Neural Networks

Autor: Sumit Diware, Koteswararao Chilakala, Rajiv V. Joshi, Said Hamdioui, Rajendra Bishnoi

Publikováno v: IEEE Access, Vol 12, Pp 47469-47482 (2024)

Diabetic retinopathy (DR) is a leading cause of permanent vision loss worldwide. It refers to irreversible retinal damage caused due to elevated glucose levels and blood pressure. Regular screening for DR can facilitate its early detection and timely

Externí odkaz: https://doaj.org/article/5bf9028c8237420ea73a3f9332eff4e1

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Akademický článek

Using Cryogenic CMOS Control Electronics to Enable a Two-Qubit Cross-Resonance Gate

Publikováno v: PRX Quantum, Vol 5, Iss 1, p 010326 (2024)

Qubit control electronics composed of CMOS circuits are of critical interest for next-generation quantum computing systems. A CMOS-based application-specific integrated circuit (ASIC) fabricated in 14-nm fin field-effect transistor (FinFET) technolog

Externí odkaz: https://doaj.org/article/b4874854c72746b1b718911c6c5a6faa

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Akademický článek

Accurate Inference With Inaccurate RRAM Devices: A Joint Algorithm-Design Solution

Autor: Gouranga Charan, Abinash Mohanty, Xiaocong Du, Gokul Krishnan, Rajiv V. Joshi, Yu Cao

Publikováno v: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, Vol 6, Iss 1, Pp 27-35 (2020)

Resistive random access memory (RRAM) is a promising technology for energy-efficient neuromorphic accelerators. However, when a pretrained deep neural network (DNN) model is programmed to an RRAM array for inference, the model suffers from accuracy d

Externí odkaz: https://doaj.org/article/ed9f39b1d39d4305b2941cd434d8fb3d

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Akademický článek

An Energy-Efficient Digital ReRAM-Crossbar-Based CNN With Bitwise Parallelism

Autor: Leibin Ni, Zichuan Liu, Hao Yu, Rajiv V. Joshi

Publikováno v: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, Vol 3, Pp 37-46 (2017)

There is great attention to develop hardware accelerator with better energy efficiency, as well as throughput, than GPUs for convolutional neural network (CNN). The existing solutions have relatively limited parallelism as well as large power consump

Externí odkaz: https://doaj.org/article/d0bcc8207ad543108222322fb767f429

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