Phil Knag

991 total citations
27 papers, 703 citations indexed

About

Phil Knag is a scholar working on Electrical and Electronic Engineering, Hardware and Architecture and Computer Vision and Pattern Recognition. According to data from OpenAlex, Phil Knag has authored 27 papers receiving a total of 703 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Electrical and Electronic Engineering, 9 papers in Hardware and Architecture and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Phil Knag's work include Advanced Memory and Neural Computing (16 papers), Ferroelectric and Negative Capacitance Devices (9 papers) and Neural dynamics and brain function (5 papers). Phil Knag is often cited by papers focused on Advanced Memory and Neural Computing (16 papers), Ferroelectric and Negative Capacitance Devices (9 papers) and Neural dynamics and brain function (5 papers). Phil Knag collaborates with scholars based in United States, South Korea and United Kingdom. Phil Knag's co-authors include Zhengya Zhang, Ram Krishnamurthy, Gregory K. Chen, Wei Lü, H. Ekin Sumbul, Raghavan Kumar, Thomas Chen, Mingoo Seok, Dewei Wang and Siddharth Gaba and has published in prestigious journals such as IEEE Transactions on Signal Processing, IEEE Journal of Solid-State Circuits and IEEE Transactions on Nuclear Science.

In The Last Decade

Phil Knag

26 papers receiving 694 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Phil Knag United States 12 590 192 134 130 109 27 703
Indranil Chakraborty United States 15 680 1.2× 202 1.1× 91 0.7× 105 0.8× 63 0.6× 36 815
Xiaoxin Cui China 11 473 0.8× 134 0.7× 78 0.6× 87 0.7× 106 1.0× 134 577
Aayush Ankit United States 16 839 1.4× 349 1.8× 139 1.0× 137 1.1× 109 1.0× 32 1.1k
Rawan Naous United States 12 777 1.3× 135 0.7× 110 0.8× 171 1.3× 162 1.5× 21 851
Syed Shakib Sarwar United States 13 673 1.1× 265 1.4× 213 1.6× 101 0.8× 81 0.7× 22 867
H. Ekin Sumbul United States 10 346 0.6× 118 0.6× 67 0.5× 65 0.5× 184 1.7× 24 522
Dhireesha Kudithipudi United States 14 625 1.1× 332 1.7× 202 1.5× 132 1.0× 74 0.7× 88 732
Gregory K. Chen United States 12 647 1.1× 132 0.7× 83 0.6× 162 1.2× 313 2.9× 22 723
Zhenhua Zhu China 17 884 1.5× 275 1.4× 45 0.3× 180 1.4× 115 1.1× 63 1.1k
Richard Linderman United States 10 387 0.7× 132 0.7× 95 0.7× 158 1.2× 74 0.7× 43 574

Countries citing papers authored by Phil Knag

Since Specialization
Citations

This map shows the geographic impact of Phil Knag'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 Phil Knag with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Phil Knag more than expected).

Fields of papers citing papers by Phil Knag

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Phil Knag. 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 Phil Knag. The network helps show where Phil Knag may publish in the future.

Co-authorship network of co-authors of Phil Knag

This figure shows the co-authorship network connecting the top 25 collaborators of Phil Knag. A scholar is included among the top collaborators of Phil Knag 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 Phil Knag. Phil Knag is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Chen, Gregory K., et al.. (2025). T-REX: Hardware–Software Co-Optimized Transformer Accelerator With Reduced External Memory Access and Enhanced Hardware Utilization. IEEE Journal of Solid-State Circuits. 61(1). 154–167.
3.
Wang, Dewei, et al.. (2023). DIMCA: An Area-Efficient Digital In-Memory Computing Macro Featuring Approximate Arithmetic Hardware in 28 nm. IEEE Journal of Solid-State Circuits. 59(3). 960–971. 11 indexed citations
4.
Chen, Gregory K., Phil Knag, Carlos Tokunaga, & Ram Krishnamurthy. (2022). An 8-core RISC-V Processor with Compute near Last Level Cache in Intel 4 CMOS. 2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits). 68–69. 3 indexed citations
5.
Wang, Dewei, et al.. (2022). DIMC: 2219TOPS/W 2569F2/b Digital In-Memory Computing Macro in 28nm Based on Approximate Arithmetic Hardware. 2022 IEEE International Solid- State Circuits Conference (ISSCC). 266–268. 68 indexed citations
6.
Chen, Gregory K., Phil Knag, Carlos Tokunaga, & Ram Krishnamurthy. (2022). An Eight-Core RISC-V Processor With Compute Near Last Level Cache in Intel 4 CMOS. IEEE Journal of Solid-State Circuits. 58(4). 1117–1128. 11 indexed citations
7.
Kumar, Raghavan, Gregory K. Chen, H. Ekin Sumbul, et al.. (2020). A 9.0-TOPS/W Hash-Based Deep Neural Network Accelerator Enabling 128× Model Compression in 10-nm FinFET CMOS. IEEE Solid-State Circuits Letters. 3. 338–341. 3 indexed citations
8.
9.
Agarwal, Amit, Steven Hsu, Mark Anders, et al.. (2020). 25.7 Time-Borrowing Fast Mux-D Scan Flip-Flop with On-Chip Timing/Power/VMIN Characterization Circuits in 10nm CMOS. 392–394. 1 indexed citations
10.
Kar, Monodeep, Amit Agarwal, Steven Hsu, et al.. (2020). A Ray-Casting Accelerator in 10nm CMOS for Efficient 3D Scene Reconstruction in Edge Robotics and Augmented Reality Applications. 1–2. 7 indexed citations
11.
Sumbul, H. Ekin, Gregory K. Chen, Phil Knag, et al.. (2020). A 2.9–33.0 TOPS/W Reconfigurable 1-D/2-D Compute-Near-Memory Inference Accelerator in 10-nm FinFET CMOS. IEEE Solid-State Circuits Letters. 3. 118–121. 5 indexed citations
12.
Knag, Phil, Gregory K. Chen, H. Ekin Sumbul, et al.. (2020). A 617-TOPS/W All-Digital Binary Neural Network Accelerator in 10-nm FinFET CMOS. IEEE Journal of Solid-State Circuits. 56(4). 1082–1092. 25 indexed citations
13.
Nurvitadhi, Eriko, Ali Jafari, Andrew Boutros, et al.. (2019). Why Compete When You Can Work Together: FPGA-ASIC Integration for Persistent RNNs. 199–207. 43 indexed citations
14.
Chen, Gregory K., Raghavan Kumar, H. Ekin Sumbul, Phil Knag, & Ram Krishnamurthy. (2018). A 4096-Neuron 1M-Synapse 3.8-pJ/SOP Spiking Neural Network With On-Chip STDP Learning and Sparse Weights in 10-nm FinFET CMOS. IEEE Journal of Solid-State Circuits. 54(4). 992–1002. 148 indexed citations
15.
Bell, John, Phil Knag, Yong Lim, et al.. (2017). A 1.5-GHz 6.144T Correlations/s 64 $\times $ 64 Cross-Correlator With 128 Integrated ADCs for Real-Time Synthetic Aperture Imaging. IEEE Journal of Solid-State Circuits. 52(5). 1450–1457. 10 indexed citations
16.
Knag, Phil, et al.. (2016). A 1.40mm2141mW 898GOPS sparse neuromorphic processor in 40nm CMOS. 1–2. 7 indexed citations
17.
Knag, Phil, et al.. (2015). A Sparse Coding Neural Network ASIC With On-Chip Learning for Feature Extraction and Encoding. IEEE Journal of Solid-State Circuits. 50(4). 1070–1079. 51 indexed citations
18.
Knag, Phil, et al.. (2014). Efficient Hardware Architecture for Sparse Coding. IEEE Transactions on Signal Processing. 62(16). 4173–4186. 6 indexed citations
19.
Kim, Jung Kuk, Phil Knag, Thomas Chen, & Zhengya Zhang. (2014). A 6.67mW sparse coding ASIC enabling on-chip learning and inference. 1–2. 12 indexed citations
20.
Knag, Phil, Wei Lü, & Zhengya Zhang. (2014). A Native Stochastic Computing Architecture Enabled by Memristors. IEEE Transactions on Nanotechnology. 13(2). 283–293. 91 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Indranil Chakraborty Breakdown of academic impact, for papers by Xiaoxin Cui Breakdown of academic impact, for papers by Aayush Ankit Breakdown of academic impact, for papers by Rawan Naous Breakdown of academic impact, for papers by Syed Shakib Sarwar Breakdown of academic impact, for papers by H. Ekin Sumbul Breakdown of academic impact, for papers by Dhireesha Kudithipudi Breakdown of academic impact, for papers by Gregory K. Chen Breakdown of academic impact, for papers by Zhenhua Zhu Breakdown of academic impact, for papers by Richard Linderman
Rankless by CCL
2026