Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Pramesh Pandey"'
Publikováno v:
Journal of Low Power Electronics and Applications, Vol 12, Iss 2, p 32 (2022)
Increasing processing requirements in the Artificial Intelligence (AI) realm has led to the emergence of domain-specific architectures for Deep Neural Network (DNN) applications. Tensor Processing Unit (TPU), a DNN accelerator by Google, has emerged
Externí odkaz:
https://doaj.org/article/898d2d7bf07f4b30b2e17a8e811dd93e
Autor:
Pramesh Pandey, Noel Daniel Gundi, Prabal Basu, Tahmoures Shabanian, Mitchell Craig Patrick, Koushik Chakraborty, Sanghamitra Roy
Publikováno v:
Journal of Low Power Electronics and Applications, Vol 10, Iss 4, p 33 (2020)
AI evolution is accelerating and Deep Neural Network (DNN) inference accelerators are at the forefront of ad hoc architectures that are evolving to support the immense throughput required for AI computation. However, much more energy efficient design
Externí odkaz:
https://doaj.org/article/f9b11576b3d84eef9635bf7b43e1e056
Autor:
Tahmoures Shabanian, Noel Daniel Gundi, Pramesh Pandey, Sanghamitra Roy, Koushik Chakraborty, Prabal Basu
Publikováno v:
IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 29:1790-1799
Modern deep neural network (DNN) applications demand a remarkable processing throughput usually unmet by traditional Von Neumann architectures. Consequently, hardware accelerators, comprising a sea of multiplier-and-accumulate (MAC) units, have recen
Publikováno v:
Electrical and Computer Engineering Faculty Publications
The emergence of hardware accelerators has brought about several orders of magnitude improvement in the speed of the deep neural-network (DNN) inference. Among such DNN accelerators, the Google tensor processing unit (TPU) has transpired to be the be
Publikováno v:
DAC
Electrical and Computer Engineering Faculty Publications
Electrical and Computer Engineering Faculty Publications
The AI boom is bringing a plethora of domain-specific architectures for Neural Network computations. Google’s Tensor Processing Unit (TPU), a Deep Neural Network (DNN) accelerator, has replaced the CPUs/GPUs in its data centers, claiming more than
Autor:
Koushik Chakraborty, Pramesh Pandey, Zhen Zhang, Prabal Basu, Noel Daniel Gundi, Tahmoures Shabanian, Sanghamitra Roy
Publikováno v:
ASP-DAC
Electrical and Computer Engineering Faculty Publications
Electrical and Computer Engineering Faculty Publications
Modern deep neural network (DNN) applications demand a remarkable processing throughput usually unmet by traditional Von Neumann architectures. Consequently, hardware accelerators, comprising a sea of multiplier and accumulate (MAC) units, have recen
Publikováno v:
DAC
The emergence of hardware accelerators has brought about several orders of magnitude improvement in the speed of the deep neural-network (DNN) inference. Among such DNN accelerators, Google Tensor Processing Unit (TPU) has transpired to be the best-i
Autor:
Koushik Chakraborty, Prabal Basu, Noel Daniel Gundi, Mitchell Craig Patrick, Sanghamitra Roy, Tahmoures Shabanian, Pramesh Pandey
Publikováno v:
Journal of Low Power Electronics and Applications, Vol 10, Iss 33, p 33 (2020)
Electrical and Computer Engineering Faculty Publications
Journal of Low Power Electronics and Applications
Volume 10
Issue 4
Electrical and Computer Engineering Faculty Publications
Journal of Low Power Electronics and Applications
Volume 10
Issue 4
AI evolution is accelerating and Deep Neural Network (DNN) inference accelerators are at the forefront of ad hoc architectures that are evolving to support the immense throughput required for AI computation. However, much more energy efficient design
Publikováno v:
ISLPED
Electrical and Computer Engineering Faculty Publications
Electrical and Computer Engineering Faculty Publications
SRAM-based PUFs (SPUFs) have emerged as promising security primitives for low-power devices. However, operating 8T-SPUFs at Near-Threshold Computing (NTC) realm is plagued by exacerbated process variation (PV) sensitivity which thwarts their reliable
Autor:
Chidhambaranathan Rajamanikkam, Trevor Carter, Prabal Basu, Koushik Chakraborty, Sanghamitra Roy, Pramesh Pandey, Aatreyi Bal
Publikováno v:
Electrical and Computer Engineering Faculty Publications
In this letter, we explore the emerging security threats of near-threshold computing (NTC). Researchers have shown that the delay sensitivity of a circuit to supply voltage variation tremendously increases, as the circuit’s operating conditions shi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::243430400df28a097a4666eebcdbc3a8
https://digitalcommons.usu.edu/ece_facpub/290
https://digitalcommons.usu.edu/ece_facpub/290