Gokul Krishnan

453 total citations
26 papers, 280 citations indexed

About

Gokul Krishnan is a scholar working on Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Gokul Krishnan has authored 26 papers receiving a total of 280 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Electrical and Electronic Engineering, 6 papers in Computer Vision and Pattern Recognition and 3 papers in Artificial Intelligence. Recurrent topics in Gokul Krishnan's work include Advanced Memory and Neural Computing (21 papers), Ferroelectric and Negative Capacitance Devices (17 papers) and CCD and CMOS Imaging Sensors (6 papers). Gokul Krishnan is often cited by papers focused on Advanced Memory and Neural Computing (21 papers), Ferroelectric and Negative Capacitance Devices (17 papers) and CCD and CMOS Imaging Sensors (6 papers). Gokul Krishnan collaborates with scholars based in United States, China and India. Gokul Krishnan's co-authors include Yu Cao, Ümit Y. Ogras, Jae-sun Seo, Sumit K. Mandal, Rajiv Joshi, Chaitali Chakrabarti, Gouranga Charan, Nathaniel C. Cady, Abinash Mohanty and Karsten Beckmann and has published in prestigious journals such as Inorganic Chemistry, IEEE Transactions on Computers and IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

In The Last Decade

Gokul Krishnan

24 papers receiving 277 citations

Peers

Countries citing papers authored by Gokul Krishnan

Since Specialization

Citations

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

Fields of papers citing papers by Gokul Krishnan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gokul Krishnan

This figure shows the co-authorship network connecting the top 25 collaborators of Gokul Krishnan. A scholar is included among the top collaborators of Gokul Krishnan 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 Gokul Krishnan. Gokul Krishnan 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

#	Work	Indexed citations
1	3-D In-Sensor Computing for Real-Time DVS Data Compression: 65-nm Hardware-Algorithm Co-Design 2024 ·IEEE Solid-State Circuits Letters ·(unknown), (unknown), Gokul Krishnan, (unknown), Jonghyun Oh, (unknown), (unknown), (unknown), Mingoo Seok, Jae-sun Seo, Yu Cao	4
2	SpikeSim: An End-to-End Compute-in-Memory Hardware Evaluation Tool for Benchmarking Spiking Neural Networks 2023 ·IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems ·(unknown), (unknown), (unknown), Gokul Krishnan, Yu Cao, Priyadarshini Panda	20
3	3D-ISC: A 65nm 3D Compatible In-Sensor Computing Accelerator with Reconfigurable Tile Architecture for Real-Time DVS Data Compression 2023 ·Gokul Krishnan, (unknown), Jonghyun Oh, (unknown), (unknown), (unknown), (unknown), (unknown), Jae-sun Seo, Mingoo Seok, Yu Cao	2
4	Photoluminescence Properties of a Chiral One-Dimensional Silver Chalcogenolate Chain 2023 ·Inorganic Chemistry ·(unknown), (unknown), Gokul Krishnan, Biswarup Pathak, Sukhendu Mandal	0
5	Big-Little Chiplets for In-Memory Acceleration of DNNs 2022 ·Gokul Krishnan, (unknown), Sumit K. Mandal, Zhenyu Wang, Chaitali Chakrabarti, Jae-sun Seo, Ümit Y. Ogras, Yu Cao	4
6	Exploring Model Stability of Deep Neural Networks for Reliable RRAM-Based In-Memory Acceleration 2022 ·IEEE Transactions on Computers ·Gokul Krishnan, Li Yang, (unknown), (unknown), (unknown), (unknown), Zheng Li, Karsten Beckmann, Rajiv Joshi, Nathaniel C. Cady, Deliang Fan, Yu Cao	8
7	COIN: Communication-Aware In-Memory Acceleration for Graph Convolutional Networks 2022 ·IEEE Journal on Emerging and Selected Topics in Circuits and Systems ·Sumit K. Mandal, Gokul Krishnan, (unknown), (unknown), Yu Cao, Ümit Y. Ogras	9
8	AI Computing in Light of 2.5D Interconnect Roadmap: Big-Little Chiplets for In-memory Acceleration 2022 ·2022 International Electron Devices Meeting (IEDM) ·Zhenyu Wang, (unknown), Gokul Krishnan, Sumit K. Mandal, (unknown), Jae-sun Seo, Chaitali Chakrabarti, Ümit Y. Ogras, Yu Cao	7
9	Robust RRAM-based In-Memory Computing in Light of Model Stability 2021 ·Gokul Krishnan, (unknown), (unknown), (unknown), (unknown), Zheng Li, Karsten Beckmann, Rajiv Joshi, Nathaniel C. Cady, Yu Cao	11
10	System-Level Benchmarking of Chiplet-based IMC Architectures for Deep Neural Network Acceleration 2021 ·Gokul Krishnan, Sumit K. Mandal, Chaitali Chakrabarti, Jae-sun Seo, Ümit Y. Ogras, Yu Cao	1
11	Design Limits of In-Memory Computing: Beyond the Crossbar 2021 ·Gokul Krishnan, (unknown), (unknown), (unknown), Karsten Beckmann, Rajiv Joshi, Nathaniel C. Cady, Yu Cao	1
12	Accurate Inference with Inaccurate RRAM Devices: Statistical Data, Model Transfer, and On-line Adaptation 2020 ·Gouranga Charan, (unknown), Karsten Beckmann, (unknown), Gokul Krishnan, Rajiv Joshi, Nathaniel C. Cady, Yu Cao	30
13	Accurate Inference With Inaccurate RRAM Devices: A Joint Algorithm-Design Solution 2020 ·IEEE Journal on Exploratory Solid-State Computational Devices and Circuits ·Gouranga Charan, Abinash Mohanty, (unknown), Gokul Krishnan, Rajiv Joshi, Yu Cao	30
14	Interconnect-Aware Area and Energy Optimization for In-Memory Acceleration of DNNs 2020 ·IEEE Design and Test ·Gokul Krishnan, Sumit K. Mandal, Chaitali Chakrabarti, Jae-sun Seo, Ümit Y. Ogras, Yu Cao	25
15	Small-world-based Structural Pruning for Efficient FPGA Inference of Deep Neural Networks 2020 ·Gokul Krishnan, Yufei Ma, Yu Cao	3
16	MNSIM 2.0: A Behavior-Level Modeling Tool for Memristor-based Neuromorphic Computing Systems 2020 ·Rare & Special e-Zone (The Hong Kong University of Science and Technology) ·Zhenhua Zhu, Hanbo Sun, (unknown), Lixue Xia, Gokul Krishnan, Guohao Dai, Dimin Niu, Xiaohong Chen, Xiaobo Sharon Hu, Yu Cao, Yuan Xie, Yu Wang, Huazhong Yang	53
17	A Latency-Optimized Reconfigurable NoC for In-Memory Acceleration of DNNs 2020 ·IEEE Journal on Emerging and Selected Topics in Circuits and Systems ·Sumit K. Mandal, Gokul Krishnan, Chaitali Chakrabarti, Jae-sun Seo, Yu Cao, Ümit Y. Ogras	19
18	Independent N and P process monitors for body bias based process corner correction 2014 ·Lawrence T. Clark, (unknown), (unknown), (unknown), Gokul Krishnan	2
19	Low power ARM® Cortex™-M0 CPU and SRAM using Deeply Depleted Channel (DDC) transistors with Vdd scaling and body bias 2013 ·Vikesh Agrawal, (unknown), (unknown), Gokul Krishnan, (unknown), (unknown), (unknown), P. Ranade, (unknown), (unknown), Lawrence T. Clark, (unknown), (unknown), (unknown), (unknown), (unknown), Takahiro Yamada, (unknown), (unknown)	4
20	Building block method: a bottom-up modular synthesis methodology for distributed compliant mechanisms 2012 ·Mechanical sciences ·Gokul Krishnan, (unknown), Sridhar Kota	9

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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