Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems | Paper Citation Record

Observation 4d5dd491-53ef-468a-9e6f-4c84d8e74023 · outbound

This paper cites Nullhop: A flexible convolutional neural network accelerator based on sparse representations of feature maps.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Nullhop: A flexible convolutional neural network accelerator based on sparse representations of feature maps

Reference 1

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Observation dd707539-9237-4d7f-9410-1616f3ee1a37 · outbound

This paper cites A signed binary multiplication technique.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems A signed binary multiplication technique

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:26.562737Z

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Observation b800793b-d039-43e2-92a3-76801a1d31b1 · outbound

This paper cites Skyrmion logic system for large- scale reversible computation.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Skyrmion logic system for large- scale reversible computation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:26.402353Z

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Observation 9356bba0-7b4d-4421-ad40-af895b6c1037 · outbound

This paper cites Flinkcl: An opencl- based in-memory computing architecture on heterogeneous cpu-gpu clusters for big data.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Flinkcl: An opencl- based in-memory computing architecture on heterogeneous cpu-gpu clusters for big data

Reference 4

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Observation d9e25348-bff7-4a55-8486-3758092ad92b · outbound

This paper cites IEEE Transactions on Systems, Man, and Cybernetics: Systems 47, 2740–2753.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems IEEE Transactions on Systems, Man, and Cybernetics: Systems 47, 2740–2753

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:26.080771Z

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Observation 3bd61873-1149-4391-9b5f-8ff7c85941f4 · outbound

This paper cites Gflink: An in- memory computing architecture on heterogeneous cpu-gpu clusters Choong et.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Gflink: An in- memory computing architecture on heterogeneous cpu-gpu clusters Choong et

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.948129Z

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Observation 7141863d-97c1-4d4d-b5ba-6ae48f7ec1af · outbound

This paper cites Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks

Reference 7

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Observation 03c84080-5f72-4f19-9143-9b0d5e40f3b7 · outbound

This paper cites Dwmacc: Accelerating shift-based cnns with domain wall memories.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Dwmacc: Accelerating shift-based cnns with domain wall memories

Reference 8

Resolution: verified exact; doi, observed 2026-08-06T21:02:23.747862Z

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Observation bb3b3e2f-b285-4220-bc04-e11a229abe32 · outbound

This paper cites The mnist database of handwritten digit images for machine learning research.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems The mnist database of handwritten digit images for machine learning research

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.920285Z

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Observation 55e7c71c-dceb-4521-a9b8-0a9b6f498540 · outbound

This paper cites Quantized deep neural networks for energy efficient hardware-based inference, in:201823rdAsiaandSouthPacificDesignAutomationConference (ASP-DAC), pp.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Quantized deep neural networks for energy efficient hardware-based inference, in:201823rdAsiaandSouthPacificDesignAutomationConference (ASP-DAC), pp

Reference 10

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T21:02:25.153335Z

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Observation ac0657fe-68b3-471c-a183-7455fe0d8467 · outbound

This paper cites Nvsim: A circuit- level performance, energy, and area model for emerging nonvolatile memory.IEEETransactionsonComputer-AidedDesignofIntegrated Circuits and Systems 31, 994–1007.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Nvsim: A circuit- level performance, energy, and area model for emerging nonvolatile memory.IEEETransactionsonComputer-AidedDesignofIntegrated Circuits and Systems 31, 994–1007

Reference 11

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Observation 169f9bc8-1c33-427a-99f7-b3b0e8a5c912 · outbound

This paper cites Neural cache: Bit-serial in-cache acceleration of deep neural networks, in: 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA), pp.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Neural cache: Bit-serial in-cache acceleration of deep neural networks, in: 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA), pp

Reference 12

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Observation 17e0a047-7db1-4263-8387-4967db8ce1bf · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T21:02:23.264761Z

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Observation bb3a3f19-1e8c-434a-914f-abde25f01035 · outbound

This paper cites Deep residual learning for image recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Deep residual learning for image recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp

Reference 14

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Observation c5be0bdf-8e5c-4fad-9f0d-78ec995df8b4 · outbound

This paper cites an unresolved cited work.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Unresolved cited work

Reference 15

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Observation e9264eb7-fdfa-4655-85bb-5860a408f42d · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 16

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Observation 546fef08-e1d9-4d3d-bad5-bf753e28d4b1 · outbound

This paper cites Essa: An energy-aware bit-serial streaming deep convolutional neu- ral network accelerator.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Essa: An energy-aware bit-serial streaming deep convolutional neu- ral network accelerator

Reference 17

Resolution: verified exact; doi, observed 2026-08-06T21:02:23.719517Z

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Observation f024902e-be04-4010-8c3c-d67b00ce5b98 · outbound

This paper cites Exploring main memory designbasedonracetrackmemorytechnology,in:2016International Great Lakes Symposium on VLSI (GLSVLSI), pp.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Exploring main memory designbasedonracetrackmemorytechnology,in:2016International Great Lakes Symposium on VLSI (GLSVLSI), pp

Reference 18

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T21:02:24.768353Z

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Observation c7319b4f-1c25-4760-b9d0-dbb760d625ab · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 19

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Observation 7630285e-48bc-4b7b-8629-4f9e3d8052b4 · outbound

This paper cites Quantizationandtrainingofneu- ral networks for efficient integer-arithmetic-only inference, in: 2018 IEEE/CVFConferenceonComputerVisionandPatternRecognition, pp.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Quantizationandtrainingofneu- ral networks for efficient integer-arithmetic-only inference, in: 2018 IEEE/CVFConferenceonComputerVisionandPatternRecognition, pp

Reference 20

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Observation 035096a1-49e1-4546-9150-79bcd6556a54 · outbound

This paper cites Accelerator-awarepruningforconvolutionalneural networks.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Accelerator-awarepruningforconvolutionalneural networks

Reference 21

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T21:02:24.572288Z

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Observation 25837cd5-897f-4b59-89f3-22322bcd84b5 · outbound

This paper cites skyrmion, in: 2018 IEEE 7th Non-Volatile Memory Systems and Applications Symposium (NVMSA), IEEE.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems skyrmion, in: 2018 IEEE 7th Non-Volatile Memory Systems and Applications Symposium (NVMSA), IEEE

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.866110Z

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Observation 9a82e51a-0a28-49ac-b337-095074c88c6f · outbound

This paper cites Compact modeling and evaluation of magnetic skyrmion- basedracetrackmemory.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Compact modeling and evaluation of magnetic skyrmion- basedracetrackmemory

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.840515Z

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Observation 2b0842ca-6d81-4c8f-b601-40426b303b16 · outbound

This paper cites Exploiting retraining-basedmixed-precisionquantizationforlow-costdnnaccel- erator design.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Exploiting retraining-basedmixed-precisionquantizationforlow-costdnnaccel- erator design

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.819712Z

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Observation ba7e98cd-fc05-40c0-842c-e582aaef4c98 · outbound

This paper cites Heterogeneous dataflow accelerators for multi-dnn work- loads,in:2021IEEEInternationalSymposiumonHigh-Performance Computer Architecture (HPCA), IEEE.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Heterogeneous dataflow accelerators for multi-dnn work- loads,in:2021IEEEInternationalSymposiumonHigh-Performance Computer Architecture (HPCA), IEEE

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.803955Z

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Observation 4d51e971-8a80-40d8-bdca-4c54a59645b0 · outbound

This paper cites Gradient-based learning applied to document recognition.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Gradient-based learning applied to document recognition

Reference 26

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Observation d2221562-5bf0-4d34-afcd-43967cf65678 · outbound

This paper cites Lognet:Energy-efficientneuralnetworksusinglogarithmiccomputa- tion, in: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Lognet:Energy-efficientneuralnetworksusinglogarithmiccomputa- tion, in: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp

Reference 27

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T21:02:24.482616Z

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Observation 6c1c24e0-f6d0-461a-9884-b071a3d712e9 · outbound

This paper cites 45nm low power cmos logic compatible embedded stt mram utilizing a reverse-connection 1t/1mtj cell, in: 2009 IEEE International Electron Devices Meeting (IEDM), pp.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems 45nm low power cmos logic compatible embedded stt mram utilizing a reverse-connection 1t/1mtj cell, in: 2009 IEEE International Electron Devices Meeting (IEDM), pp

Reference 28

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T21:02:24.389311Z

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Observation e253ea87-9409-42cd-b24c-c8036e37249b · outbound

This paper cites an unresolved cited work.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:02:25.789714Z

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Observation a029ac4f-76b6-471a-913d-c6e6ac030a2d · outbound

This paper cites Skyrmion devices for memory and logic applications.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Skyrmion devices for memory and logic applications

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.775049Z

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Observation a11dfb94-7458-43da-8385-2bd533c4fc93 · outbound

This paper cites A novel two- stage modular multiplier based on racetrack memory for asymmet- ric cryptography, in: 2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), IEEE.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems A novel two- stage modular multiplier based on racetrack memory for asymmet- ric cryptography, in: 2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), IEEE

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.759436Z

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Observation 9471dead-e800-4949-b915-6d153664471e · outbound

This paper cites An fpga-based hardware emulator for neuromorphic chip with rram.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems An fpga-based hardware emulator for neuromorphic chip with rram

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.743553Z

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Observation 8d50418c-c5bd-4a9c-9804-05c3165c47cb · outbound

This paper cites Energy efficient in-memory integer multiplication based on racetrack mem- ory, in: 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS), IEEE.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Energy efficient in-memory integer multiplication based on racetrack mem- ory, in: 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS), IEEE

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.727259Z

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Observation a7fb11ec-c84b-4f78-8ead-70771e40e399 · outbound

This paper cites A racetrack memory based in-memory booth multiplier for cryptography application, in: 2016 21st Asia and South Pacific Design Automation Conference (ASP-DAC), IEEE.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems A racetrack memory based in-memory booth multiplier for cryptography application, in: 2016 21st Asia and South Pacific Design Automation Conference (ASP-DAC), IEEE

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.710086Z

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Observation d4f0848b-dd52-4eb1-a522-fdf68950ca60 · outbound

This paper cites Towards energy-proportional datacenter memory with mobile dram, in: 2012 39th Annual International Sym- posium on Computer Architecture (ISCA), pp.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Towards energy-proportional datacenter memory with mobile dram, in: 2012 39th Annual International Sym- posium on Computer Architecture (ISCA), pp

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Resolution: malformed identifier; raw_fallback, observed 2026-08-06T21:02:24.234838Z

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Observation d13926e3-0ccd-4216-90c2-2702aa3c6c4b · outbound

This paper cites Exploration of gpgpu register file architecture using domain-wall-shift-write based racetrack memory, in: Design Automation Conference (DAC), 2014 51st ACM/EDAC/IEEE, pp.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Exploration of gpgpu register file architecture using domain-wall-shift-write based racetrack memory, in: Design Automation Conference (DAC), 2014 51st ACM/EDAC/IEEE, pp

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.693943Z

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Observation 9f9f6a39-4d5e-4c7e-b5ea-27f3766b3eb9 · outbound

This paper cites An energy- efficient gpgpu register file architecture using racetrack memory.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems An energy- efficient gpgpu register file architecture using racetrack memory

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.676900Z

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Observation d7340a19-b320-48f7-9cc7-3809aca2db51 · outbound

This paper cites Applied Physics Express 1, 091301.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Applied Physics Express 1, 091301

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.659046Z

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Observation b6acf619-4a46-49ef-863c-515e29cb4fde · outbound

This paper cites Zigzag: Enlarging joint architecture-mapping design space explo- ration for dnn accelerators.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Zigzag: Enlarging joint architecture-mapping design space explo- ration for dnn accelerators

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.642699Z

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Observation 9750e082-c255-453f-bf49-a1fd9493a0db · outbound

This paper cites A spintronics full adder for magnetic cpu.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems A spintronics full adder for magnetic cpu

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.626851Z

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Observation 999d3ed9-21d1-426a-ac0e-0255a1e96c17 · outbound

This paper cites Anenergy-efficientprecision-scalable convnetprocessorin40-nmcmos.IEEEJournalofsolid-stateCircuits 52, 903–914.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Anenergy-efficientprecision-scalable convnetprocessorin40-nmcmos.IEEEJournalofsolid-stateCircuits 52, 903–914

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.610021Z

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Observation b86a601b-88e9-4172-8d2b-e99f292ec56f · outbound

This paper cites Magneticdomain-wall racetrack memory.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Magneticdomain-wall racetrack memory

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T21:02:23.432953Z

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Observation c2df5e6a-8c5d-400b-a87f-eb8e024ba4a4 · outbound

This paper cites Pytorch: An imper- ative style, high-performance deep learning library, in: Advances in Neural Information Processing Systems 32.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Pytorch: An imper- ative style, high-performance deep learning library, in: Advances in Neural Information Processing Systems 32

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.593860Z

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Observation 85fa95c3-0627-4cb8-b8b9-57134022cfa1 · outbound

This paper cites Parallelcom- putation in the racetrack memory.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Parallelcom- putation in the racetrack memory

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.577437Z

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Observation cbe88c31-ec94-48d0-825e-2865c47166e6 · outbound

This paper cites ImageNet Large Scale Visual Recognition Challenge.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems ImageNet Large Scale Visual Recognition Challenge

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T21:02:23.447290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13fba473-2544-421e-b75d-6440f1e460b8 · outbound

This paper cites an unresolved cited work.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:02:25.561407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6c148211-7cd9-46b5-af2f-55418749c4da · outbound

This paper cites 45nm freepdk library URL: https://si2.org/open-cell-library/.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems 45nm freepdk library URL: https://si2.org/open-cell-library/

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.544587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 31f020c5-71cf-4fa5-9917-80aace26d7fe · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T21:02:23.462478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4c77d8d-7a53-4289-bcac-f5791df9edc3 · outbound

This paper cites Stt-ram buffer design for precision-tunable general-purpose neural network accelerator.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Stt-ram buffer design for precision-tunable general-purpose neural network accelerator

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.528704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b220154b-6d2c-45af-81f8-3f24865e7723 · outbound

This paper cites Cross-layer racetrack memory design for ultra high density and low power consumption, in: Design AutomationConference(DAC),201350thACM/EDAC/IEEE,pp.1– 6.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Cross-layer racetrack memory design for ultra high density and low power consumption, in: Design AutomationConference(DAC),201350thACM/EDAC/IEEE,pp.1– 6

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.513719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 30c35044-882a-400c-8493-0fcd2496895b · outbound

This paper cites Magnetic adder based on racetrack memory.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Magnetic adder based on racetrack memory

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.497068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3210ed56-23c6-4a35-bb35-c0ce5e9e8a45 · outbound

This paper cites Stag: Spintronic-tape architecture for gpgpu cache hierarchies, in: Computer Architecture (ISCA), 2014 ACM/IEEE 41st International Symposium on, pp.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Stag: Spintronic-tape architecture for gpgpu cache hierarchies, in: Computer Architecture (ISCA), 2014 ACM/IEEE 41st International Symposium on, pp

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.480334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ca63c2d5-8b3a-4e31-ba5c-eeed2a6d63b4 · outbound

This paper cites Dwm- tapestri-an energy efficient all-spin cache using domain wall shift based writes, in: Proceedings of the Conference on Design, Automa- tion and Test in Europe, EDA Consortium.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Dwm- tapestri-an energy efficient all-spin cache using domain wall shift based writes, in: Proceedings of the Conference on Design, Automa- tion and Test in Europe, EDA Consortium

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.463771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e7a198a6-19c7-454c-9442-eba02b7c8b22 · outbound

This paper cites Ultra-dense ring-shaped racetrack memory cache design.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Ultra-dense ring-shaped racetrack memory cache design

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.446496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a9f2c575-1316-444d-a3ab-558ce8b06f8c · outbound

This paper cites An automatic- addressingarchitecturewithfullyserializedaccessinracetrackmem- ory for energy-efficient cnns.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems An automatic- addressingarchitecturewithfullyserializedaccessinracetrackmem- ory for energy-efficient cnns

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.429917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 99147cc2-e130-4c3c-bdc7-2fd4a39b180c · outbound

This paper cites Dw-aes: A domain- wall nanowire-based aes for high throughput and energy-efficient data encryption in non-volatile memory.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Dw-aes: A domain- wall nanowire-based aes for high throughput and energy-efficient data encryption in non-volatile memory

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.412753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b6d29d07-08f3-475f-8785-5e07f993dac6 · outbound

This paper cites Energy efficient in- memory aes encryption based on nonvolatile domain-wall nanowire, in:Design,AutomationandTestinEuropeConferenceandExhibition (DATE), 2014, IEEE.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Energy efficient in- memory aes encryption based on nonvolatile domain-wall nanowire, in:Design,AutomationandTestinEuropeConferenceandExhibition (DATE), 2014, IEEE

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.396959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8869f74e-05df-4a37-b0b8-a6b8e4f3a0e6 · outbound

This paper cites Multilane racetrack caches: Improving efficiency through compression and independent shifting, in: Design Automation Conference (ASP-DAC), 2015 20th Asia and South Pacific, IEEE.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Multilane racetrack caches: Improving efficiency through compression and independent shifting, in: Design Automation Conference (ASP-DAC), 2015 20th Asia and South Pacific, IEEE

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.379381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cb963cd8-5bae-4a10-9ef0-7d2693aca709 · outbound

This paper cites Designing energy-efficient convolutional neural networks using energy-aware pruning, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Designing energy-efficient convolutional neural networks using energy-aware pruning, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp

Reference 59

Resolution: verified exact; doi, observed 2026-08-06T21:02:23.666851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3a45556b-fe70-467b-93c3-76aa48fc9e84 · outbound

This paper cites an unresolved cited work.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:02:25.362877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation dc403beb-c65d-4501-a60b-dd69571ea792 · outbound

This paper cites Energy-efficient nonvolatile reconfigurable logic using spin hall effect-based lookup tables.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Energy-efficient nonvolatile reconfigurable logic using spin hall effect-based lookup tables

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.346822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5ebbc993-c421-4cb3-bd04-456f0abdc5aa · outbound

This paper cites Quantitative modeling of racetrack memory, a tradeoff among area, performance, and power, in: The 20th Asia and South Pacific Design Automation Conference, pp.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Quantitative modeling of racetrack memory, a tradeoff among area, performance, and power, in: The 20th Asia and South Pacific Design Automation Conference, pp

Reference 62

Resolution: verified exact; doi, observed 2026-08-06T21:02:23.642823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6adb23a0-2be9-4ebf-a9f6-6adddf6ce6b7 · outbound

This paper cites In-memory computation of a machine-learning classifier in a standard 6t sram array.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems In-memory computation of a machine-learning classifier in a standard 6t sram array

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T21:02:23.558712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4aa87ee-7c6b-4409-8fea-674706e3b2c1 · outbound

This paper cites Magnetic skyrmion logic gates: conversion, duplication and merging of skyrmions.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Magnetic skyrmion logic gates: conversion, duplication and merging of skyrmions

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.329870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d4d3deb6-2e7b-4902-b5e7-0f26a3e1ad61 · outbound

This paper cites Perpendicular-magnetic-anisotropy cofeb racetrack mem- ory.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Perpendicular-magnetic-anisotropy cofeb racetrack mem- ory

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.312039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d973c1d1-a73e-477c-af4c-633985f159c1 · outbound

This paper cites an unresolved cited work.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:02:25.294212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 07b68028-6b05-4d77-984e-c9024c23bdd2 · outbound

This paper cites Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T21:02:23.586520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e19465bb-adb0-4592-b9c9-ad55c477110a · outbound

This paper cites An efficient hardware accelerator for structured sparse convolutional neural networks on fpgas.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems An efficient hardware accelerator for structured sparse convolutional neural networks on fpgas

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:02:25.260674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7a0b2cc2-d6ea-47c5-9882-bf437c88bdaa · outbound

This paper cites an unresolved cited work.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Unresolved cited work

Reference 240

Resolution: verified exact; doi, observed 2026-08-06T21:02:23.764836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8a577fe4-9db1-4b28-a4c6-174d2704e398 · outbound

This paper cites an unresolved cited work.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Unresolved cited work

Reference 1477

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T21:02:24.107735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation dda02881-3381-42d8-9aca-65db0f5658ad · outbound

This paper cites an unresolved cited work.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Unresolved cited work

Reference 2013

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:02:25.277052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8aaf2473-e3b4-43a0-980a-a3da37336fad · outbound

This paper cites an unresolved cited work.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Unresolved cited work

Reference 2014

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T21:02:24.002239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ad26b7a0-48ea-4710-a3cc-f8153d9b27df · outbound

This paper cites an unresolved cited work.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Unresolved cited work

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-06T21:02:23.367018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3eb766d5-5308-4790-9a1f-79c3242d8ce0 · outbound

This paper cites an unresolved cited work.

Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems Unresolved cited work

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-06T21:02:23.281854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers