Zobrazeno 1 - 10
of 72
pro vyhledávání: '"Amit Ranjan Trivedi"'
Autor:
Changhyeon Lee, Leila Rahimifard, Junhwan Choi, Jeong-ik Park, Chungryeol Lee, Divake Kumar, Priyesh Shukla, Seung Min Lee, Amit Ranjan Trivedi, Hocheon Yoo, Sung Gap Im
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract Probabilistic inference in data-driven models is promising for predicting outputs and associated confidence levels, alleviating risks arising from overconfidence. However, implementing complex computations with minimal devices still remains
Externí odkaz:
https://doaj.org/article/fbfcc4e40a1c44fbad736369add4619c
Autor:
Shamma Nasrin, Ahish Shylendra, Nastaran Darabi, Theja Tulabandhula, Wilfred Gomes, Ankush Chakrabarty, Amit Ranjan Trivedi
Publikováno v:
IEEE Access, Vol 10, Pp 81447-81457 (2022)
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 years, novel hardware-friendly inference operators such as binary-weight,
Externí odkaz:
https://doaj.org/article/566feb4a1a1346518a4bcd19560df7a5
Publikováno v:
IEEE Access, Vol 8, Pp 216259-216270 (2020)
This paper presents a hardware management technique that enables energy-efficient acceleration of deep neural networks (DNNs) on realtime-constrained embedded edge devices. It becomes increasingly common for edge devices to incorporate dedicated hard
Externí odkaz:
https://doaj.org/article/90ea5134965d486f9679abdf6026d4b2
Publikováno v:
IEEE Internet of Things Journal. 9:24615-24627
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d67398d61ac15527351778c6d81b82ac
https://doi.org/10.1109/jiot.2022.3195546
https://doi.org/10.1109/jiot.2022.3195546
Publikováno v:
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 41:2107-2114
Ferroelectric field-effect transistor-based circuit implementation mimicking FitzHugh-Nagumo neuron is proposed in this work. The proposed circuit is shown to mimic biological neuron properties such as excitation block and anodal break excitation whi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d0d4060ba1c0e92d7512084318495f81
https://doi.org/10.1109/tcad.2021.3101407
https://doi.org/10.1109/tcad.2021.3101407
Publikováno v:
2023 24th International Symposium on Quality Electronic Design (ISQED).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3614c0d84fac9986414d0967eb9640fe
https://doi.org/10.1109/isqed57927.2023.10129331
https://doi.org/10.1109/isqed57927.2023.10129331
Autor:
Amit Ranjan Trivedi, Sawyer B. Fuller, Theja Tulabandhula, Priyesh Shukla, Ankith Muralidhar, Nick Iliev
Publikováno v:
IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 30:68-80
We propose a novel compute-in-memory (CIM)-based ultralow-power framework for probabilistic localization of insect-scale drones. Localization is a critical subroutine for path planning and rotor control in drones, where a drone is required to continu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::188da44a4ec02359dcad7d9cf254d84f
https://doi.org/10.1109/tvlsi.2021.3100252
https://doi.org/10.1109/tvlsi.2021.3100252
Publikováno v:
2023 36th International Conference on VLSI Design and 2023 22nd International Conference on Embedded Systems (VLSID).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f2bdb67de649bfd74871d92aba4b4bb6
https://doi.org/10.1109/vlsid57277.2023.00029
https://doi.org/10.1109/vlsid57277.2023.00029
Publikováno v:
2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e41d032c7e555ac8712aab154f376c9d
https://doi.org/10.1109/icecs202256217.2022.9971009
https://doi.org/10.1109/icecs202256217.2022.9971009
Autor:
Silu Guo, William A. Gaviria Rojas, Mark C. Hersam, Hadallia Bergeron, Stephanie E. Liu, Shamma Nasrin, Ahish Shylendra, Hong Sub Lee, Amit Ranjan Trivedi, Shaowei Li, Jiangtan Yuan, Vinod K. Sangwan
Publikováno v:
Nano Letters. 21:6432-6440
Artificial intelligence and machine learning are growing computing paradigms, but current algorithms incur undesirable energy costs on conventional hardware platforms, thus motivating the exploration of more efficient neuromorphic architectures. Towa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9622b4f9be663aa8adb7ea5bc8697151
https://doi.org/10.1021/acs.nanolett.1c00982
https://doi.org/10.1021/acs.nanolett.1c00982