Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Bahar Asgari"'
Publikováno v:
2022 32nd International Conference on Field-Programmable Logic and Applications (FPL).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::55ec7bfb6b5d27ac18df3a2bb56388ec
https://doi.org/10.1109/fpl57034.2022.00049
https://doi.org/10.1109/fpl57034.2022.00049
Publikováno v:
IEEE Transactions on Computers. 70:524-538
Scientific computations with a wide range of applications in domains such as developing vaccines, forecasting the weather, predicting natural disasters, simulating aerodynamics of spacecraft, and exploring oil resources, create the main workloads of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::25e1517f76f764c34eae87aecd384875
https://doi.org/10.1109/tc.2021.3060700
https://doi.org/10.1109/tc.2021.3060700
Publikováno v:
Journal of Signal Processing Systems. 93:659-675
As computations in machine-learning applications are increasing simultaneously along the size of datasets, the energy and performance costs of data movement dominate that of compute. This issue is more pronounced in embedded systems with limited reso
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2cdc582652688d299b5488bedbf293c0
https://doi.org/10.1007/s11265-019-01505-1
https://doi.org/10.1007/s11265-019-01505-1
Publikováno v:
IEEE Micro. 39:46-54
Systolic arrays with promising attributes, such as high degree of concurrent computation and high data-reuse rate, are attractive solutions for dense linear algebra. Recently, systolic arrays have been used for accelerating the inference of deep neur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cf951060383a5de224fe628793e91853
https://doi.org/10.1109/mm.2019.2930057
https://doi.org/10.1109/mm.2019.2930057
Publikováno v:
HPCA
Memory-bound sparse gathering, caused by irregular random memory accesses, has become an obstacle in several on-demand applications such as embedding lookup in recommendation systems. To reduce the amount of data movement, and thereby better utilize
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5d4985945a4081d6a76eaada7a6fe2c9
https://doi.org/10.1109/hpca51647.2021.00080
https://doi.org/10.1109/hpca51647.2021.00080
Publikováno v:
ASPLOS
With fully autonomous flight capabilities coupled with user-specific applications, drones, in particular quadcopter drones, are becoming prevalent solutions in a myriad of commercial and research contexts. However, autonomous drones must operate with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f5be3dbe477f1049a139ae29dd1a488
Publikováno v:
ICCD
The fundamental building block of many algorithms such as data analytics and neural networks is matrix multiplication. Besides its popularity, matrix multiplication is one of the rare algebraic computations that demand high data reuse rate. During th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cf0d25dc91f760751ff7ad00673eb9e1
https://doi.org/10.1109/iccd50377.2020.00036
https://doi.org/10.1109/iccd50377.2020.00036
Publikováno v:
DAC
A key real-time task in autonomous systems is simultaneous localization and mapping (SLAM). Although prior work has proposed hardware accelerators to process SLAM in real time, they paid less attention to power consumption. To be more power-efficient
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3e26c9e0bde7d5eac8d29239ac6ce29c
https://doi.org/10.1109/dac18072.2020.9218550
https://doi.org/10.1109/dac18072.2020.9218550
Publikováno v:
FCCM
Matrix multiplication (MM) has several applications in fields such as statistics, physics, economics, and computer science. For instance, the main computation behind deep neural networks (DNNs) is a convolution that can be implemented as MM. The incr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::86d1676dbf9260844f42ed34b92b89a0
https://doi.org/10.1109/fccm48280.2020.00035
https://doi.org/10.1109/fccm48280.2020.00035
Publikováno v:
DATE
Sparse computations dominate a wide range of applications from scientific problems to graph analytics. The main characterization of sparse computations, indirect memory accesses, prevents them from effectively achieving high performance on general-pu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ac850cdeb297e81afa0f2c4a91566f3b
https://doi.org/10.23919/date48585.2020.9116501
https://doi.org/10.23919/date48585.2020.9116501