A Machine Learning Accelerator In-Memory for Energy Harvesting | Paper Citation Record

Observation f169a299-1ab0-47e0-a95e-0bbb08e48a2d · outbound

This paper cites Accessed: 2019-08-10.

A Machine Learning Accelerator In-Memory for Energy Harvesting Accessed: 2019-08-10

Reference 1

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Observation 2a4c4cf3-e819-4385-bd01-50e48bee2eae · outbound

This paper cites A public domain dataset for human activity recognition using smartphones.

A Machine Learning Accelerator In-Memory for Energy Harvesting A public domain dataset for human activity recognition using smartphones

Reference 2

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Observation 5019b2dd-9604-4735-a498-e1a95b6f460b · outbound

This paper cites Incremental checkpointing of program state to nvram for transiently-powered systems.

A Machine Learning Accelerator In-Memory for Energy Harvesting Incremental checkpointing of program state to nvram for transiently-powered systems

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:12.108418Z

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Observation 692ff895-c9e3-4a50-b659-f2ac66553d75 · outbound

This paper cites Graceful performance modulation for power-neutral transient computing systems.

A Machine Learning Accelerator In-Memory for Energy Harvesting Graceful performance modulation for power-neutral transient computing systems

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:12.096206Z

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Observation 4af04a5e-b155-4ec7-aa6d-b69274a1dece · outbound

This paper cites Hibernus++: a self-calibrating and adaptive system for transiently-powered embedded devices.

A Machine Learning Accelerator In-Memory for Energy Harvesting Hibernus++: a self-calibrating and adaptive system for transiently-powered embedded devices

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:12.084075Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9bd29a29-f09a-4336-8404-9e5a485d5228 · outbound

This paper cites Hi- bernus: Sustaining computation during intermittent supply for energy-harvesting systems.

A Machine Learning Accelerator In-Memory for Energy Harvesting Hi- bernus: Sustaining computation during intermittent supply for energy-harvesting systems

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:12.069655Z

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Observation 6c6d1001-d261-4d0b-89d9-2439f7ba589d · outbound

This paper cites Peripheral state persistence for transiently-powered systems.

A Machine Learning Accelerator In-Memory for Energy Harvesting Peripheral state persistence for transiently-powered systems

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:12.056183Z

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Observation 20b378fa-57ac-4f61-a285-0987d3256a06 · outbound

This paper cites Next generation micro-power systems.

A Machine Learning Accelerator In-Memory for Energy Harvesting Next generation micro-power systems

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:12.043184Z

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Observation 8a9222a9-ef54-4ccd-a634-c8d4ac7b7a2c · outbound

This paper cites Libsvm: A library for support vector machines.

A Machine Learning Accelerator In-Memory for Energy Harvesting Libsvm: A library for support vector machines

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:12.031448Z

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Observation d5b9a917-dafc-44ee-9fa2-797b872dd139 · outbound

This paper cites Eﬃcient in-memory processing using spintronics.

A Machine Learning Accelerator In-Memory for Energy Harvesting Eﬃcient in-memory processing using spintronics

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:12.019587Z

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Observation 1a6fdc07-e6d8-4fe6-b7d5-a5037aa2e1a8 · outbound

This paper cites Chain: tasks and channels for reliable intermittent programs.

A Machine Learning Accelerator In-Memory for Energy Harvesting Chain: tasks and channels for reliable intermittent programs

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:12.007582Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c38038e6-07d1-4670-98bf-70f0ac1fc7bd · outbound

This paper cites Termination checking and task decomposition for task-based intermittent programs.

A Machine Learning Accelerator In-Memory for Energy Harvesting Termination checking and task decomposition for task-based intermittent programs

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.994308Z

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Observation ee9b989e-48bd-4c42-b259-388d15dd5b74 · outbound

This paper cites A reconﬁgurable energy storage architecture for energy-harvesting devices.

A Machine Learning Accelerator In-Memory for Energy Harvesting A reconﬁgurable energy storage architecture for energy-harvesting devices

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.981174Z

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Observation 3ffc19e6-b189-4983-8ca2-1908fa792927 · outbound

This paper cites Xnor neural engine: A hardware accelerator ip for 21.6-fj/op binary neural network inference.

A Machine Learning Accelerator In-Memory for Energy Harvesting Xnor neural engine: A hardware accelerator ip for 21.6-fj/op binary neural network inference

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.968636Z

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Observation 797e409c-0af9-4195-ad40-a93e77e35cd7 · outbound

This paper cites Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1.

A Machine Learning Accelerator In-Memory for Energy Harvesting Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T10:34:11.005584Z

Source-reported events for the cited work

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Observation 083a1c79-bd59-42c6-86c4-1da63c204a60 · outbound

This paper cites Spin transfer switching in dual mgo magnetic tunnel junctions.

A Machine Learning Accelerator In-Memory for Energy Harvesting Spin transfer switching in dual mgo magnetic tunnel junctions

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.956653Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2508e66d-6765-4ece-8c21-23e72758522b · outbound

This paper cites Circuit and microarchitecture evaluation of 3d stacking magnetic ram (mram) as a universal memory replacement.

A Machine Learning Accelerator In-Memory for Energy Harvesting Circuit and microarchitecture evaluation of 3d stacking magnetic ram (mram) as a universal memory replacement

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.944405Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 325ef71c-fc6e-4f29-a8c9-ed1a2b7cfe86 · outbound

This paper cites Nvsim: A circuit-level performance, energy, and area model for emerging nonvolatile memory.

A Machine Learning Accelerator In-Memory for Energy Harvesting Nvsim: A circuit-level performance, energy, and area model for emerging nonvolatile memory

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.930792Z

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Observation d654c271-5fc3-438e-9d2d-e0f87a6d166e · outbound

This paper cites Tunable damping, saturation magnetization, and exchange stiﬀness of half-heusler nimnsb thin ﬁlms.

A Machine Learning Accelerator In-Memory for Energy Harvesting Tunable damping, saturation magnetization, and exchange stiﬀness of half-heusler nimnsb thin ﬁlms

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.919026Z

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Observation e30829cf-f298-430f-bbee-8569ecc3d7c9 · outbound

This paper cites Neural cache: Bit-serial in-cache acceleration of deep neural networks.

A Machine Learning Accelerator In-Memory for Energy Harvesting Neural cache: Bit-serial in-cache acceleration of deep neural networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.907875Z

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Observation a9fde08d-87cc-4043-a975-027c1cd7f7ad · outbound

This paper cites The what’s next intermittent computing architec- ture.

A Machine Learning Accelerator In-Memory for Energy Harvesting The what’s next intermittent computing architec- ture

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.895961Z

Source-reported events for the cited work

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Observation 5c172cb5-eeba-4ccf-8dc5-8a1ef7681f60 · outbound

This paper cites Intermittent deep neural network inference, 2018.

A Machine Learning Accelerator In-Memory for Energy Harvesting Intermittent deep neural network inference, 2018

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.883640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 99ee7010-88ea-4ff1-bc67-a3ed4ad3eacb · outbound

This paper cites Intelligence beyond the edge: Inference on intermittent embedded systems.

A Machine Learning Accelerator In-Memory for Energy Harvesting Intelligence beyond the edge: Inference on intermittent embedded systems

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.870552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5334130e-49b3-4bf5-9695-b4eca6083fac · outbound

This paper cites Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique.

A Machine Learning Accelerator In-Memory for Energy Harvesting Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.857247Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 203a7664-dcc0-4422-94cb-e5257e767857 · outbound

This paper cites Amulet: An energy-eﬃcient, multi-application wearable platform.

A Machine Learning Accelerator In-Memory for Energy Harvesting Amulet: An energy-eﬃcient, multi-application wearable platform

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.843818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2d4b1003-774e-44d1-bdb1-ab3cc420abd8 · outbound

This paper cites Tragedy of the coulombs: Federating energy storage for tiny, intermittently-powered sensors.

A Machine Learning Accelerator In-Memory for Energy Harvesting Tragedy of the coulombs: Federating energy storage for tiny, intermittently-powered sensors

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.831788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6256611a-2079-43b4-8261-13f7971e2fa5 · outbound

This paper cites Flicker: Rapid prototyping for the batteryless internet-of-things.

A Machine Learning Accelerator In-Memory for Energy Harvesting Flicker: Rapid prototyping for the batteryless internet-of-things

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.818474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f420f22c-8854-471b-b19f-94024dbd2ea2 · outbound

This paper cites Timely execution on intermittently powered batteryless sensors.

A Machine Learning Accelerator In-Memory for Energy Harvesting Timely execution on intermittently powered batteryless sensors

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.806960Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8c06da42-30a4-45f9-aa81-59447ba22e2b · outbound

This paper cites Clank: Architectural support for intermittent computation.

A Machine Learning Accelerator In-Memory for Energy Harvesting Clank: Architectural support for intermittent computation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.794347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b6122e9a-3135-4fce-bf53-7ffcfee24886 · outbound

This paper cites Stt-mram with double magnetic tunnel junctions.

A Machine Learning Accelerator In-Memory for Energy Harvesting Stt-mram with double magnetic tunnel junctions

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.782417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation eb9f9a00-d2a7-4a81-9520-3bdf3e0c47e0 · outbound

This paper cites Quickrecall: A low overhead hw/sw approach for enabling computations across power cycles in transiently powered computers.

A Machine Learning Accelerator In-Memory for Energy Harvesting Quickrecall: A low overhead hw/sw approach for enabling computations across power cycles in transiently powered computers

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.770504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0724cb81-0802-477a-ab28-f6ec37195d6b · outbound

This paper cites Ambient rf energy-harvesting technologies for self-sustainable standalone wireless sensor platforms.

A Machine Learning Accelerator In-Memory for Energy Harvesting Ambient rf energy-harvesting technologies for self-sustainable standalone wireless sensor platforms

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.757016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6c85f928-1813-4bde-9fa9-d36006284705 · outbound

This paper cites Scaling up the accuracy of naive-bayes classiﬁers: A decision-tree hybrid.

A Machine Learning Accelerator In-Memory for Energy Harvesting Scaling up the accuracy of naive-bayes classiﬁers: A decision-tree hybrid

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.743043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8d818c86-efd4-45b4-affe-0e2f1abdab2a · outbound

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

A Machine Learning Accelerator In-Memory for Energy Harvesting Gradient-based learning applied to document recognition

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.728812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 40e37cdd-b82b-4433-bd9d-0fbde13f3d23 · outbound

This paper cites Pinatubo: A processing-in-memory architecture for bulk bitwise operations in emerging non-volatile memories.

A Machine Learning Accelerator In-Memory for Energy Harvesting Pinatubo: A processing-in-memory architecture for bulk bitwise operations in emerging non-volatile memories

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.715246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 91b773ad-727d-4cb1-9c5c-9478c0d9b86f · outbound

This paper cites Pudiannao: A polyvalent machine learning accelerator.

A Machine Learning Accelerator In-Memory for Energy Harvesting Pudiannao: A polyvalent machine learning accelerator

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.585465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7d98b2f7-21fa-455a-9a7b-ef5ac23c2919 · outbound

This paper cites Lightweight hardware support for transparent consistency-aware checkpointing in intermittent energy-harvesting systems.

A Machine Learning Accelerator In-Memory for Energy Harvesting Lightweight hardware support for transparent consistency-aware checkpointing in intermittent energy-harvesting systems

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.571756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a0175455-8a53-483d-88c2-5c8ec3101782 · outbound

This paper cites Ambient energy harvesting nonvolatile processors: from circuit to system.

A Machine Learning Accelerator In-Memory for Energy Harvesting Ambient energy harvesting nonvolatile processors: from circuit to system

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.557548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0ef4f500-0f40-4847-a098-73f4642993aa · outbound

This paper cites A simpler, safer programming and execution model for intermittent systems.

A Machine Learning Accelerator In-Memory for Energy Harvesting A simpler, safer programming and execution model for intermittent systems

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.544108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 394c15ee-b38a-4263-9db3-40f23ce289b4 · outbound

This paper cites Using sleep states to maximize the active time of transient computing systems.

A Machine Learning Accelerator In-Memory for Energy Harvesting Using sleep states to maximize the active time of transient computing systems

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.531337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bb36c93d-cc5d-4525-ba3b-7c3c860cc0df · outbound

This paper cites Incidental computing on iot nonvolatile processors.

A Machine Learning Accelerator In-Memory for Energy Harvesting Incidental computing on iot nonvolatile processors

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.519610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 18a8c43b-05a0-4eea-8358-9b5603a92986 · outbound

This paper cites Architecture exploration for ambient energy harvesting nonvolatile processors.

A Machine Learning Accelerator In-Memory for Energy Harvesting Architecture exploration for ambient energy harvesting nonvolatile processors

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.506942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 61b6c6e7-2bbf-49c6-93bd-15c594583100 · outbound

This paper cites Alpaca: intermittent execution without checkpoints.

A Machine Learning Accelerator In-Memory for Energy Harvesting Alpaca: intermittent execution without checkpoints

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.495105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 22c0c72c-b5f2-4622-aa6f-a17642c6f94c · outbound

This paper cites Adaptive dynamic checkpointing for safe eﬃcient intermittent computing.

A Machine Learning Accelerator In-Memory for Energy Harvesting Adaptive dynamic checkpointing for safe eﬃcient intermittent computing

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.483536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3fe926d0-57f4-4276-bc82-cdd247e00000 · outbound

This paper cites Intelligent buildings of the future: Cyberaware, deep learning powered, and human interacting.

A Machine Learning Accelerator In-Memory for Energy Harvesting Intelligent buildings of the future: Cyberaware, deep learning powered, and human interacting

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.472438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 97e546e5-2e06-4088-87bf-f72974b2dc32 · outbound

This paper cites Low damping constant for co 2 feal heusler alloy ﬁlms and its correlation with density of states.

A Machine Learning Accelerator In-Memory for Energy Harvesting Low damping constant for co 2 feal heusler alloy ﬁlms and its correlation with density of states

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.460423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0ae44f75-8729-4132-9218-81c4d8d524a2 · outbound

This paper cites R: A Language and Environment for Statistical Computing.

A Machine Learning Accelerator In-Memory for Energy Harvesting R: A Language and Environment for Statistical Computing

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.447485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a2258fd6-a6ac-4954-8949-5729964d9a5b · outbound

This paper cites Mementos: system support for long-running computation on rﬁd-scale devices.

A Machine Learning Accelerator In-Memory for Energy Harvesting Mementos: system support for long-running computation on rﬁd-scale devices

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.435790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b254177d-3d4f-4fcf-aa76-787984f8c2b1 · outbound

This paper cites Transactional concurrency control for intermittent, energy-harvesting computing systems.

A Machine Learning Accelerator In-Memory for Energy Harvesting Transactional concurrency control for intermittent, energy-harvesting computing systems

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.422600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 026800ef-5291-4673-ba0c-26761e2fc0c1 · outbound

This paper cites Sub-3 ns pulse with sub-100 µa switching of 1x–2x nm perpendicular mtj for high-performance embedded stt-mram towards sub-20 nm cmos.

A Machine Learning Accelerator In-Memory for Energy Harvesting Sub-3 ns pulse with sub-100 µa switching of 1x–2x nm perpendicular mtj for high-performance embedded stt-mram towards sub-20 nm cmos

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.409808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 88e191fb-b0a7-4373-88f4-051c36cdcee2 · outbound

This paper cites Design of an rﬁd-based battery-free programmable sensing platform.

A Machine Learning Accelerator In-Memory for Energy Harvesting Design of an rﬁd-based battery-free programmable sensing platform

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.397802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 00e5fa64-92d6-4b10-b041-6edc922df6c9 · outbound

This paper cites The eh model: Early design space exploration of intermittent processor architectures.

A Machine Learning Accelerator In-Memory for Energy Harvesting The eh model: Early design space exploration of intermittent processor architectures

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.385579Z

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Observation a3aa9127-7b0d-4b85-bd7a-e8eabc3bb6d1 · outbound

This paper cites Properties of magnetic tunnel junctions with a mgo/cofeb/ta/cofeb/mgo recording structure down to junction diameter of 11 nm.

A Machine Learning Accelerator In-Memory for Energy Harvesting Properties of magnetic tunnel junctions with a mgo/cofeb/ta/cofeb/mgo recording structure down to junction diameter of 11 nm

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.372016Z

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Observation 7fc29162-d430-4f88-b205-f010e7e5a7c3 · outbound

This paper cites Ambit: In-memory accelerator for bulk bitwise oper- ations using commodity dram technology.

A Machine Learning Accelerator In-Memory for Energy Harvesting Ambit: In-memory accelerator for bulk bitwise oper- ations using commodity dram technology

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.357351Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a319e6f5-00ca-4d63-ae14-44b219d9988c · outbound

This paper cites A 462gops/j rram-based nonvolatile intelligent processor for energy harvesting ioe system featuring nonvolatile logics and processing-in-memory.

A Machine Learning Accelerator In-Memory for Energy Harvesting A 462gops/j rram-based nonvolatile intelligent processor for energy harvesting ioe system featuring nonvolatile logics and processing-in-memory

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.345668Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f7a84c4d-3722-4883-a5c6-de90fe36bac9 · outbound

This paper cites Fully parallel rram synaptic array for implementing binary neural network with (+ 1,- 1) weights and (+ 1, 0) neurons.

A Machine Learning Accelerator In-Memory for Energy Harvesting Fully parallel rram synaptic array for implementing binary neural network with (+ 1,- 1) weights and (+ 1, 0) neurons

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.333783Z

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Observation 0bed7077-29f9-42fe-ba68-0606a0dc8e52 · outbound

This paper cites Binary convolutional neural network on rram.

A Machine Learning Accelerator In-Memory for Energy Harvesting Binary convolutional neural network on rram

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.319911Z

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Observation cfe4de87-2bd7-4fb8-a0c0-5e7b427bee0d · outbound

This paper cites Accessed: 2019- 06-02.

A Machine Learning Accelerator In-Memory for Energy Harvesting Accessed: 2019- 06-02

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.307587Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f46f9383-9951-400b-a9e7-413b0c5f8d92 · outbound

This paper cites Intermittent computation without hardware support or programmer intervention.

A Machine Learning Accelerator In-Memory for Energy Harvesting Intermittent computation without hardware support or programmer intervention

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.294686Z

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Observation 542501a9-ac15-4a3c-b680-527049cb7c4a · outbound

This paper cites Magnetic tunnel junction based integrated logics and computational circuits.

A Machine Learning Accelerator In-Memory for Energy Harvesting Magnetic tunnel junction based integrated logics and computational circuits

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.280794Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4f53730e-862d-4a09-b383-b5aaba919159 · outbound

This paper cites Switched by input: power eﬃcient structure for rram-based convolutional neural network.

A Machine Learning Accelerator In-Memory for Energy Harvesting Switched by input: power eﬃcient structure for rram-based convolutional neural network

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.267907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8886e248-afc9-4288-8c2a-e28a5231da30 · outbound

This paper cites Binary neural network with 16 mb rram macro chip for classiﬁcation and online training.

A Machine Learning Accelerator In-Memory for Energy Harvesting Binary neural network with 16 mb rram macro chip for classiﬁcation and online training

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.255667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cf4dfb40-e243-48df-83fe-4376edabc636 · outbound

This paper cites Using spin-hall mtjs to build an energy-eﬃcient in-memory computation platform.

A Machine Learning Accelerator In-Memory for Energy Harvesting Using spin-hall mtjs to build an energy-eﬃcient in-memory computation platform

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:34:11.242967Z

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