Akshitha Sriraman

Computer Networks and Communications · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 19 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Networks and Communications — 27 citations into it; #5,288 of 9,886 by trust here, #7,140 by citations
• Hardware and Architecture — 19 citations into it
• Information Systems — 18 citations into it; #4,385 of 8,771 by trust here, #6,501 by citations
• Artificial Intelligence — 8 citations into it
• Electrical and Electronic Engineering — 6 citations into it
• ML Systems & Hardware — 6 citations into it; #321 of 575 by trust here, #499 by citations
• Computer Vision and Pattern Recognition — 5 citations into it

Whom Akshitha Sriraman trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Guilherme Ottoni
2. Tuomas Pelkonen
3. Svilen Kanev
4. A. Belay
5. Sunpyo Hong
6. Kim M. Hazelwood
7. Emilee J. Rader
8. Scott Franklin
9. Michael Ferdman
10. Michael Bedford Taylor

Who trusts Akshitha Sriraman overall

1. Haoran Qiu
2. Abhishek Dhanotia
3. Subho Sankar Banerjee
4. Saurabh Jha

Papers from Akshitha Sriraman's perspective

Ordered by how strongly the researchers Akshitha Sriraman trusts endorse each paper. Same corpus, a different order for every researcher.

1. In-datacenter performance analysis of a tensor processing unit
2. Web Search for a Planet: The Google Cluster Architecture (IEEE Micro)
3. Tradeoffs between power management and tail latency in warehouse-scale applications
4. A reconfigurable fabric for accelerating large-scale datacenter services
5. CPI2: CPU performance isolation for shared compute clusters (European Conference on Computer Systems)
6. Paragon: QoS-aware scheduling for heterogeneous datacenters (International Conference on Architectural Support for Programming Languages and Operating Systems)
7. Dune: Safe User-level Access to Privileged CPU Features (USENIX Symposium on Operating Systems Design and Implementation)
8. Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective
9. Practical Lessons from Predicting Clicks on Ads at Facebook
10. Bubble-up: Increasing utilization in modern warehouse scale computers via sensible co-locations (Micro)

Their papers

• Accelerometer: Understanding Acceleration Opportunities for Data Center Overheads at Hyperscale (2020) — 123 citations
• SoftSKU: Optimizing Server Architectures for Microservice Diversity @Scale (2019) — 115 citations