Phil Knag

1.0k citations

28 papers · 757 · h-index 13

Impact in

Hardware and Architecture top 5%
Electrical and Electronic Engineering top 10%
- Advanced Memory and Neural Computing
- Ferroelectric and Negative Capacitance Devices
- CCD and CMOS Imaging Sensors
- Low-power high-performance VLSI design

Papers in

Electrical and Electronic Engineering 24
- Advanced Memory and Neural Computing 17
- Ferroelectric and Negative Capacitance Devices 10
- Low-power high-performance VLSI design 5
- Semiconductor materials and devices 4
Hardware and Architecture 9
- VLSI and Analog Circuit Testing 4
- Parallel Computing and Optimization Techniques 4

Co-authors: Zhengya Zhang (10 shared papers)Ram Krishnamurthy (18 shared papers)Gregory K. Chen (13 shared papers)Wei Lü (3 shared papers)H. Ekin Sumbul (12 shared papers)Raghavan Kumar (12 shared papers)Thomas Chen (5 shared papers)Mingoo Seok (4 shared papers)
Journals: IEEE Journal of Solid-State Circuits (7 papers)IEEE Solid-State Circuits Letters (2 papers)IEEE Transactions on Nuclear Science (1 paper)IEEE Transactions on Nanotechnology (1 paper)IEEE Transactions on Signal Processing (1 paper)
Partner nations: United States South Korea United Kingdom

In The Last Decade

Phil Knag

27 papers receiving 749 citations

Peers

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-authors

The 25 scholars most cited alongside Phil Knag, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Phil Knag Line = papers co-authored together Phil Knag links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#	Work
1	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 ·Gregory K. Chen, Raghavan Kumar, H. Ekin Sumbul, Phil Knag, Ram Krishnamurthy	2018	158
2	A Native Stochastic Computing Architecture Enabled by Memristors IEEE Transactions on Nanotechnology ·Phil Knag, Wei Lü, Zhengya Zhang	2014	96
3	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) ·Dewei Wang, Chuan-Tung Lin, Gregory K. Chen, Phil Knag, Ram Krishnamurthy, Mingoo Seok	2022	77
4	A 640M pixel/s 3.65mW sparse event-driven neuromorphic object recognition processor with on-chip learning Jung Kuk Kim, Phil Knag, Thomas Chen, Zhengya Zhang	2015	64
5	Memristive devices for stochastic computing Siddharth Gaba, Phil Knag, Zhengya Zhang, Wei Lü	2014	55
6	A Sparse Coding Neural Network ASIC With On-Chip Learning for Feature Extraction and Encoding IEEE Journal of Solid-State Circuits ·Phil Knag, Jung Kuk Kim, Thomas Chen, Zhengya Zhang	2015	52
7	Why Compete When You Can Work Together: FPGA-ASIC Integration for Persistent RNNs Eriko Nurvitadhi, Dongup Kwon, Ali Jafari, Andrew Boutros, Jaewoong Sim, Phillip Tomson, H. Ekin Sumbul, Gregory Chen, Phil Knag, Raghavan Kumar, Ram Krishnamurthy, Sergey Gribok, Bogdan Pasca, Martin Langhammer, Debbie Marr, Aravind Dasu	2019	45
8	Characterization of Heavy-Ion-Induced Single-Event Effects in 65 nm Bulk CMOS ASIC Test Chips IEEE Transactions on Nuclear Science ·Chia‐Hsiang Chen, Phil Knag, Zhengya Zhang	2014	36
9	A 617-TOPS/W All-Digital Binary Neural Network Accelerator in 10-nm FinFET CMOS IEEE Journal of Solid-State Circuits ·Phil Knag, Gregory K. Chen, H. Ekin Sumbul, Raghavan Kumar, Steven K. Hsu, Amit Agarwal, Monodeep Kar, Seongjong Kim, Mark Anders, Himanshu Kaul, Ram Krishnamurthy	2020	28
10	A 4096-Neuron 1M-Synapse 3.8PJ/SOP Spiking Neural Network with On-Chip STDP Learning and Sparse Weights in 10NM FinFET CMOS Gregory K. Chen, Raghavan Kumar, H. Ekin Sumbul, Phil Knag, Ram Krishnamurthy	2018	20
11	A 617 TOPS/W All Digital Binary Neural Network Accelerator in 10nm FinFET CMOS Phil Knag, Gregory K. Chen, H. Ekin Sumbul, Raghavan Kumar, Mark Anders, Himanshu Kaul, Steven K. Hsu, Amit Agarwal, Monodeep Kar, Seongjong Kim, Ram Krishnamurthy	2020	20
12	An Eight-Core RISC-V Processor With Compute Near Last Level Cache in Intel 4 CMOS IEEE Journal of Solid-State Circuits ·Gregory K. Chen, Phil Knag, Carlos Tokunaga, Ram Krishnamurthy	2022	15
13	DIMCA: An Area-Efficient Digital In-Memory Computing Macro Featuring Approximate Arithmetic Hardware in 28 nm IEEE Journal of Solid-State Circuits ·Chuan-Tung Lin, Dewei Wang, Bo Zhang, Gregory K. Chen, Phil Knag, Ram Krishnamurthy, Mingoo Seok	2023	13
14	A 6.67mW sparse coding ASIC enabling on-chip learning and inference Jung Kuk Kim, Phil Knag, Thomas Chen, Zhengya Zhang	2014	12
15	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 ·John Bell, Phil Knag, Shuanghong Sun, Yong Lim, Thomas Chen, Jeffrey A. Fredenburg, Chia‐Hsiang Chen, Chunyang Zhai, Aaron Z. Rocca, Nicholas Collins, Andres A. Tamez, Jorge Pernillo, Justin M. Correll, Alan Tanner, Zhengya Zhang, Michael P. Flynn	2017	10
16	A 1.40mm²141mW 898GOPS sparse neuromorphic processor in 40nm CMOS Phil Knag, Chester Liu, Zhengya Zhang	2016	7
17	A Ray-Casting Accelerator in 10nm CMOS for Efficient 3D Scene Reconstruction in Edge Robotics and Augmented Reality Applications Monodeep Kar, Amit Agarwal, Steven Hsu, David Moloney, Gregory Chen, Raghavan Kumar, H. Ekin Sumbul, Phil Knag, Mark Anders, Himanshu Kaul, Jonathan Byrne, Luca Sarti, Ram Krishnamurthy, Vivek De	2020	7
18	Efficient Hardware Architecture for Sparse Coding IEEE Transactions on Signal Processing ·Jung Kuk Kim, Phil Knag, Thomas Chen, Zhengya Zhang	2014	6
19	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 ·H. Ekin Sumbul, Gregory K. Chen, Phil Knag, Raghavan Kumar, Mark Anders, Himanshu Kaul, Steven K. Hsu, Amit Agarwal, Monodeep Kar, Seongjong Kim, Ram Krishnamurthy	2020	6
20	RRAM Solutions for Stochastic Computing Phil Knag, Siddharth Gaba, Wei Lü, Zhengya Zhang	2019	6

About Phil Knag

Phil Knag is a scholar working on Electrical and Electronic Engineering, Hardware and Architecture, Computer Vision and Pattern Recognition, Cognitive Neuroscience and Artificial Intelligence, having authored 28 papers that have together received 757 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (17 papers), Ferroelectric and Negative Capacitance Devices (10 papers), Neural dynamics and brain function (5 papers), Low-power high-performance VLSI design (5 papers), Advanced Neural Network Applications (4 papers), Semiconductor materials and devices (4 papers), VLSI and Analog Circuit Testing (4 papers) and Parallel Computing and Optimization Techniques (4 papers). The work is most often cited by research in Hardware and Architecture (117 citations), Electrical and Electronic Engineering (629 citations), Cellular and Molecular Neuroscience (135 citations), Cognitive Neuroscience (138 citations) and Artificial Intelligence (210 citations). Phil Knag has collaborated with scholars based in United States, South Korea and United Kingdom. Frequent co-authors include Zhengya Zhang, Ram Krishnamurthy, Gregory K. Chen, Wei Lü, H. Ekin Sumbul, Raghavan Kumar, Thomas Chen, Mingoo Seok, Siddharth Gaba and Dewei Wang. Their work appears in journals such as IEEE Journal of Solid-State Circuits, IEEE Solid-State Circuits Letters, IEEE Transactions on Nuclear Science, IEEE Transactions on Nanotechnology and IEEE Transactions on Signal Processing.

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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