Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Talati, Nishil"'
Counting the number of small subgraphs, called motifs, is a fundamental problem in social network analysis and graph mining. Many real-world networks are directed and temporal, where edges have timestamps. Motif counting in directed, temporal graphs
Externí odkaz:
http://arxiv.org/abs/2409.08975
Due to the cost-prohibitive nature of training Large Language Models (LLMs), fine-tuning has emerged as an attractive alternative for specializing LLMs for specific tasks using limited compute resources in a cost-effective manner. In this paper, we c
Externí odkaz:
http://arxiv.org/abs/2408.04693
The "AI for Science, Energy, and Security" report from DOE outlines a significant focus on developing and optimizing artificial intelligence workflows for a foundational impact on a broad range of DOE missions. With the pervasive usage of artificial
Externí odkaz:
http://arxiv.org/abs/2404.06675
Autor:
Chen, Yuhan, Ye, Haojie, Vedula, Sanketh, Bronstein, Alex, Dreslinski, Ronald, Mudge, Trevor, Talati, Nishil
Graph sparsification is a technique that approximates a given graph by a sparse graph with a subset of vertices and/or edges. The goal of an effective sparsification algorithm is to maintain specific graph properties relevant to the downstream task w
Externí odkaz:
http://arxiv.org/abs/2311.12314
Temporal motif mining is the task of finding the occurrences of subgraph patterns within a large input temporal graph that obey the specified structural and temporal constraints. Despite its utility in several critical application domains that demand
Externí odkaz:
http://arxiv.org/abs/2310.02800
Vector processing has become commonplace in today's CPU microarchitectures. Vector instructions improve performance and energy which is crucial for resource-constraint mobile devices. The research community currently lacks a comprehensive benchmark s
Externí odkaz:
http://arxiv.org/abs/2309.02680
Autor:
Yang, Yichen, Li, Jingtao, Talati, Nishil, Pal, Subhankar, Feng, Siying, Chakrabarti, Chaitali, Mudge, Trevor, Dreslinski, Ronald
The irregular nature of memory accesses of graph workloads makes their performance poor on modern computing platforms. On manycore reconfigurable architectures (MRAs), in particular, even state-of-the-art graph prefetchers do not work well (only 3% s
Externí odkaz:
http://arxiv.org/abs/2301.12312
Publikováno v:
In Microelectronics Journal June 2015 46(6):551-562
Akademický článek
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Autor:
Mane, Pravin1 pravinmane@goa.bits-pilani.ac.in, Talati, Nishil1, Riswadkar, Ameya1, Raghu, Ramesh1, Ramesha, C1
Publikováno v:
Sādhanā: Academy Proceedings in Engineering Sciences. Jan2017, Vol. 42 Issue 1, p33-44. 12p.