Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Suvadeep Banerjee"'
Autor:
Ayush Arunachalam, Shamik Kundu, Arnab Raha, Suvadeep Banerjee, Suriyaprakash Natarajan, Kanad Basu
Publikováno v:
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 42:1085-1098
Publikováno v:
IEEE Design & Test. 40:90-99
Publikováno v:
IEEE Transactions on Computers. :1-15
Autor:
Susmita Dey Manasi, Suvadeep Banerjee, Abhijit Davare, Anton A. Sorokin, Steven M. Burns, Desmond A. Kirkpatrick, Sachin S. Sapatnekar
Publikováno v:
Proceedings of the 28th Asia and South Pacific Design Automation Conference.
Autor:
Ayush Arunachalam, Athulya Kizhakkayil, Shamik Kundu, Arnab Raha, Suvadeep Banerjee, Robert Jin, Fei Su, Kanad Basu
Publikováno v:
2022 IEEE International Test Conference (ITC).
Publikováno v:
2022 IEEE International Test Conference (ITC).
Publikováno v:
IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 29:485-498
High accuracy and ever-increasing computing power have made deep neural networks (DNNs) the algorithm of choice for various machine learning, computer vision, and image processing applications across the computing spectrum. To this end, Google develo
Publikováno v:
IEEE Transactions on Dependable and Secure Computing. 18:576-592
Successful deployment of autonomous systems in a wide range of societal applications depends on error-free operation of the underlying signal processing and control functions. Real-time error detection in nonlinear systems has mostly relied on redund
Publikováno v:
2022 IEEE 40th VLSI Test Symposium (VTS).
Autor:
Ayush Arunachalam, Arnab Raha, Kanad Basu, Suriyaprakash Natarajan, Suvadeep Banerjee, Shamik Kundu
Publikováno v:
ISQED
The ever-increasing computing requirements of Deep Neural Networks (DNNs) have accentuated the deployment of such networks on hardware accelerators. Inference execution of large DNNs often manifests as an energy bottleneck in such accelerators, espec