Zobrazeno 1 - 10
of 38
pro vyhledávání: '"SHAHROODI, TAHA"'
Autor:
Shahroodi, Taha, Cardoso, Raphael, Wong, Stephan, Bosio, Alberto, O'Connor, Ian, Hamdioui, Said
State-of-the-Art (SotA) hardware implementations of Deep Neural Networks (DNNs) incur high latencies and costs. Binary Neural Networks (BNNs) are potential alternative solutions to realize faster implementations without losing accuracy. In this paper
Externí odkaz:
http://arxiv.org/abs/2401.17724
Autor:
Patel, Minesh, Shahroodi, Taha, Manglik, Aditya, Yağlıkçı, Abdullah Giray, Olgun, Ataberk, Luo, Haocong, Mutlu, Onur
Generational improvements to commodity DRAM throughout half a century have long solidified its prevalence as main memory across the computing industry. However, overcoming today's DRAM technology scaling challenges requires new solutions driven by bo
Externí odkaz:
http://arxiv.org/abs/2401.16279
With the recent move towards sequencing of accurate long reads, finding solutions that support efficient analysis of these reads becomes more necessary. The long execution time required for sequence alignment of long reads negatively affects genomic
Externí odkaz:
http://arxiv.org/abs/2310.15634
Autor:
Shahroodi, Taha, Singh, Gagandeep, Zahedi, Mahdi, Mao, Haiyu, Lindegger, Joel, Firtina, Can, Wong, Stephan, Mutlu, Onur, Hamdioui, Said
Basecalling, an essential step in many genome analysis studies, relies on large Deep Neural Networks (DNNs) to achieve high accuracy. Unfortunately, these DNNs are computationally slow and inefficient, leading to considerable delays and resource cons
Externí odkaz:
http://arxiv.org/abs/2310.04366
Applications of Binary Neural Networks (BNNs) are promising for embedded systems with hard constraints on computing power. Contrary to conventional neural networks with the floating-point datatype, BNNs use binarized weights and activations which add
Externí odkaz:
http://arxiv.org/abs/2211.06261
Autor:
Firtina, Can, Pillai, Kamlesh, Kalsi, Gurpreet S., Suresh, Bharathwaj, Cali, Damla Senol, Kim, Jeremie, Shahroodi, Taha, Cavlak, Meryem Banu, Lindegger, Joel, Alser, Mohammed, Luna, Juan Gómez, Subramoney, Sreenivas, Mutlu, Onur
Profile hidden Markov models (pHMMs) are widely employed in various bioinformatics applications to identify similarities between biological sequences, such as DNA or protein sequences. In pHMMs, sequences are represented as graph structures. These pr
Externí odkaz:
http://arxiv.org/abs/2207.09765
Autor:
Shahroodi, Taha, Zahedi, Mahdi, Firtina, Can, Alser, Mohammed, Wong, Stephan, Mutlu, Onur, Hamdioui, Said
Food profiling is an essential step in any food monitoring system needed to prevent health risks and potential frauds in the food industry. Significant improvements in sequencing technologies are pushing food profiling to become the main computationa
Externí odkaz:
http://arxiv.org/abs/2206.01932
Autor:
Patel, Minesh, Shahroodi, Taha, Manglik, Aditya, Yaglikci, A. Giray, Olgun, Ataberk, Luo, Haocong, Mutlu, Onur
Today's systems have diverse needs that are difficult to address using one-size-fits-all commodity DRAM. Unfortunately, although system designers can theoretically adapt commodity DRAM chips to meet their particular design goals (e.g., by reducing ac
Externí odkaz:
http://arxiv.org/abs/2204.10378
Autor:
Firtina, Can, Park, Jisung, Alser, Mohammed, Kim, Jeremie S., Cali, Damla Senol, Shahroodi, Taha, Ghiasi, Nika Mansouri, Singh, Gagandeep, Kanellopoulos, Konstantinos, Alkan, Can, Mutlu, Onur
Publikováno v:
NAR Genomics and Bioinformatics, vol. 5, no. 1, p. lqad004, Mar. 2023
Generating the hash values of short subsequences, called seeds, enables quickly identifying similarities between genomic sequences by matching seeds with a single lookup of their hash values. However, these hash values can be used only for finding ex
Externí odkaz:
http://arxiv.org/abs/2112.08687
Autor:
Bera, Rahul, Kanellopoulos, Konstantinos, Nori, Anant V., Shahroodi, Taha, Subramoney, Sreenivas, Mutlu, Onur
Past research has proposed numerous hardware prefetching techniques, most of which rely on exploiting one specific type of program context information (e.g., program counter, cacheline address) to predict future memory accesses. These techniques eith
Externí odkaz:
http://arxiv.org/abs/2109.12021