Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Ahish, Shylendra"'
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
Autor:
Megan E. Beck, Ahish Shylendra, Vinod K. Sangwan, Silu Guo, William A. Gaviria Rojas, Hocheon Yoo, Hadallia Bergeron, Katherine Su, Amit R. Trivedi, Mark C. Hersam
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-8 (2020)
Designing high performance, scalable, and energy efficient spiking neural networks remains a challenge. Here, the authors utilize mixed-dimensional dual-gated Gaussian heterojunction transistors from single-walled carbon nanotubes and monolayer MoS2
Externí odkaz:
https://doaj.org/article/4bbd6d5c2b1f4d8f83394ac1c32b1319
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
Autor:
Leila Rahimifard, Ahish Shylendra, Shamma Nasrin, Stephanie E. Liu, Vinod K. Sangwan, Mark C. Hersam, Amit Ranjan Trivedi
Publikováno v:
Frontiers in Electronic Materials. 2
The increasing complexity of deep learning systems has pushed conventional computing technologies to their limits. While the memristor is one of the prevailing technologies for deep learning acceleration, it is only suited for classical learning laye
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7c2a34db24b14a62d136bc74cdd1447e
https://doi.org/10.3389/femat.2022.950487
https://doi.org/10.3389/femat.2022.950487
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
Publikováno v:
2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::697c6e57492b70c41527e791dc83c7b2
https://doi.org/10.1109/aicas54282.2022.9869914
https://doi.org/10.1109/aicas54282.2022.9869914
Publikováno v:
2022 IEEE International Symposium on Circuits and Systems (ISCAS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::22935b2426d80b5028dd0a2d809094bb
https://doi.org/10.1109/iscas48785.2022.9937564
https://doi.org/10.1109/iscas48785.2022.9937564
Autor:
Amit Ranjan Trivedi, David J. Frank, Takashi Ando, Madhu Padmanabha Sumangala, Ahish Shylendra
Publikováno v:
IEEE Electron Device Letters. 41:1396-1399
In this work, we discuss the mitigation of threshold voltage ( ${V}_{{TH}}$ ) variability in nanoscale FinFET by exploiting the interaction of oxygen vacancies (OV) against metal gate granularity (MGG). Deposition of a metal gate on high- $\kappa $ d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::81982750234e51352303ec727cb813d1
https://doi.org/10.1109/led.2020.3009335
https://doi.org/10.1109/led.2020.3009335
Publikováno v:
IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 28:1833-1843
This article presents anomaly detection by examining sensor stream statistics (AEGIS), a novel mixed-signal framework for real-time AEGIS. AEGIS utilizes kernel density estimation (KDE)-based nonparametric density estimation to generate a real-time s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::240467300926cd7a0e0319eb57e917fa
https://doi.org/10.1109/tvlsi.2020.2984472
https://doi.org/10.1109/tvlsi.2020.2984472
An Intrinsic and Database-Free Authentication by Exploiting Process Variation in Back-End Capacitors
Publikováno v:
IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 27:1253-1261
Detection of counterfeit chips has emerged as a crucial concern. Physically unclonable function (PUF)-based techniques are widely used for authentication; however, these require dedicated hardware and large signature database. In this paper, we show
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::11a4e001fd78d5bb3a3764986590391b
https://doi.org/10.1109/tvlsi.2019.2903807
https://doi.org/10.1109/tvlsi.2019.2903807