William J. Song

635 total citations
33 papers, 459 citations indexed

About

William J. Song is a scholar working on Hardware and Architecture, Electrical and Electronic Engineering and Computer Networks and Communications. According to data from OpenAlex, William J. Song has authored 33 papers receiving a total of 459 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Hardware and Architecture, 20 papers in Electrical and Electronic Engineering and 12 papers in Computer Networks and Communications. Recurrent topics in William J. Song's work include Parallel Computing and Optimization Techniques (24 papers), Low-power high-performance VLSI design (7 papers) and Advanced Data Storage Technologies (7 papers). William J. Song is often cited by papers focused on Parallel Computing and Optimization Techniques (24 papers), Low-power high-performance VLSI design (7 papers) and Advanced Data Storage Technologies (7 papers). William J. Song collaborates with scholars based in United States, South Korea and Canada. William J. Song's co-authors include Sudhakar Yalamanchili, Saibal Mukhopadhyay, Ali Sheikholeslami, Dae‐Young Lee, Sungho Choi, Jong‐Moon Chung, Hyeonjin Kim, Hyesoon Kim, Ji‐Eun Lim and Wonyong Sung and has published in prestigious journals such as IEEE Access, IEEE Communications Magazine and IEEE Transactions on Computers.

In The Last Decade

William J. Song

32 papers receiving 445 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
William J. Song United States	13	267	237	228	40	32	33	459
Amir Mahdi Hosseini Monazzah Iran	17	355 1.3×	199 0.8×	424 1.9×	50 1.3×	23 0.7×	46	658
Changyun Zhu Canada	10	345 1.3×	216 0.9×	153 0.7×	23 0.6×	26 0.8×	16	445
Michele Petracca United States	15	506 1.9×	212 0.9×	282 1.2×	26 0.7×	14 0.4×	32	699
Brent Keeth United States	5	318 1.2×	254 1.1×	212 0.9×	11 0.3×	52 1.6×	10	466
D. Wendel United States	11	315 1.2×	418 1.8×	296 1.3×	10 0.3×	48 1.5×	25	632
Mike Ignatowski Canada	10	212 0.8×	287 1.2×	308 1.4×	10 0.3×	20 0.6×	13	459
Jeff Draper United States	13	519 1.9×	487 2.1×	321 1.4×	15 0.4×	28 0.9×	36	781
Kyu-Myung Choi South Korea	14	485 1.8×	304 1.3×	158 0.7×	11 0.3×	18 0.6×	62	637
Ilya Ganusov United States	10	252 0.9×	346 1.5×	266 1.2×	9 0.2×	14 0.4×	14	476
Ki‐Young Choi South Korea	13	342 1.3×	506 2.1×	410 1.8×	21 0.5×	74 2.3×	34	726

Countries citing papers authored by William J. Song

Since Specialization

Citations

This map shows the geographic impact of William J. Song's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by William J. Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William J. Song more than expected).

Fields of papers citing papers by William J. Song

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by William J. Song. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by William J. Song. The network helps show where William J. Song may publish in the future.

Co-authorship network of co-authors of William J. Song

This figure shows the co-authorship network connecting the top 25 collaborators of William J. Song. A scholar is included among the top collaborators of William J. Song based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with William J. Song. William J. Song is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Kim, Hyeonjin, et al.. (2025). Graphite: Hardware-Aware GNN Reshaping for Acceleration With GPU Tensor Cores. IEEE Transactions on Parallel and Distributed Systems. 36(5). 918–931.

Park, Chanho, et al.. (2023). NeuroSpector: Systematic Optimization of Dataflow Scheduling in DNN Accelerators. IEEE Transactions on Parallel and Distributed Systems. 34(8). 2279–2294. 2 indexed citations

Kim, Young‐In, Hyeonjin Kim, & William J. Song. (2023). NOMAD: Enabling Non-blocking OS-managed DRAM Cache via Tag-Data Decoupling. 193–205. 6 indexed citations

Kim, Hyeonjin & William J. Song. (2023). LAS: Locality-Aware Scheduling for GEMM-Accelerated Convolutions in GPUs. IEEE Transactions on Parallel and Distributed Systems. 34(5). 1479–1494. 9 indexed citations

Trivedi, Amit Ranjan, et al.. (2022). RelSim: Computational Framework for Lifetime Reliability Assessment of Heterogeneous Accelerator Systems. SSRN Electronic Journal. 1 indexed citations

Trivedi, Amit Ranjan, et al.. (2020). Energy-Efficient Acceleration of Deep Neural Networks on Realtime-Constrained Embedded Edge Devices. IEEE Access. 8. 216259–216270. 16 indexed citations

Kim, Hyeonjin, et al.. (2020). Duplo: Lifting Redundant Memory Accesses of Deep Neural Networks for GPU Tensor Cores. 725–737. 14 indexed citations

Song, William J., Saibal Mukhopadhyay, & Sudhakar Yalamanchili. (2016). Amdahl's law for lifetime reliability scaling in heterogeneous multicore processors. 594–605. 4 indexed citations

Song, William J., et al.. (2016). Reliability-performance tradeoffs between 2.5D and 3D-stacked DRAM processors. MY–2. 4 indexed citations

10.

Song, William J., Saibal Mukhopadhyay, & Sudhakar Yalamanchili. (2015). KitFox: Multiphysics Libraries for Integrated Power, Thermal, and Reliability Simulations of Multicore Microarchitecture. IEEE Transactions on Components Packaging and Manufacturing Technology. 5(11). 1590–1601. 6 indexed citations

11.

Song, William J., Saibal Mukhopadhyay, & Sudhakar Yalamanchili. (2015). Managing performance-reliability tradeoffs in multicore processors. 3C.1.1–3C.1.7. 12 indexed citations

12.

Wang, Jun, Thomas M. Conte, Chad Kersey, et al.. (2014). Manifold: A parallel simulation framework for multicore systems. 106–115. 44 indexed citations

13.

Chung, Jong‐Moon, et al.. (2013). Enhancements to FPMIPv6 for improved seamless vertical handover between LTE and heterogeneous access networks. IEEE Wireless Communications. 20(3). 112–119. 13 indexed citations

14.

Cho, Minki, et al.. (2013). Post-Silicon Characterization and On-Line Prediction of Transient Thermal Field in Integrated Circuits Using Thermal System Identification. IEEE Transactions on Components Packaging and Manufacturing Technology. 4(1). 37–45. 3 indexed citations

15.

Cho, Minki, William J. Song, Sudhakar Yalamanchili, & Saibal Mukhopadhyay. (2012). Thermal system identification (TSI): A methodology for post-silicon characterization and prediction of the transient thermal field in multicore chips. 118–124. 10 indexed citations

16.

Song, William J., Sudhakar Yalamanchili, Arun Rodrigues, & Saibal Mukhopadhyay. (2012). Instruction-based energy estimation methodology for asymmetric manycore processor simulations. 166–171. 3 indexed citations

17.

Song, William J., et al.. (2012). A power capping controller for multicore processors. 4709–4714. 15 indexed citations

18.

Song, William J., et al.. (2012). Throughput regulation in multicore processors via IPA. 7267–7272. 10 indexed citations

19.

Riesen, R., et al.. (2011). SST: A Scalable Parallel Framework for Architecture-Level Performance, Power, Area and Thermal Simulation. The Computer Journal. 55(2). 181–191. 8 indexed citations

20.

Song, William J., et al.. (2009). Improvements to seamless vertical handover between mobile WiMAX and 3GPP UTRAN through the evolved packet core. IEEE Communications Magazine. 47(4). 66–73. 44 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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