Paper Citation Record · LEDGER

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training

As of 15 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2411.19430.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.19430 v2

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:15:28.570537Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

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verified fuzzy31
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 76b5f9e4-90ad-4aa8-9e83-e5b7daa9423f · outbound

This paper cites Tensorflow: A system for large-scale machine learning.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Tensorflow: A system for large-scale machine learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:29.474934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6bdb7ef4-0f32-4937-ab0d-1a48001e88bb · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Pytorch: An imperative style, high-performance deep learning library

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:29.453286Z

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Observation 7216feb9-48fa-40c7-b519-da0f23c1df2f · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 3

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Observation 1d3b312e-77db-4ebe-a566-9b8eaadf9e9d · outbound

This paper cites Deep neural networks are more accurate than humans at detecting sexual orientation from facial images.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Deep neural networks are more accurate than humans at detecting sexual orientation from facial images

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:29.420409Z

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Observation f94a51a3-be16-416b-8359-e048336d118c · outbound

This paper cites Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T10:15:28.361589Z

Source-reported events for the cited work

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Observation 8c36f3d8-d93a-4762-bf78-b7b3e4f4e88e · outbound

This paper cites Sequence to sequence learning with neural networks.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Sequence to sequence learning with neural networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T10:15:28.368800Z

Source-reported events for the cited work

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Observation c7f7ade0-5b7c-47e5-848b-1afd4198031e · outbound

This paper cites Core placement optimization for multichip many-core neural network systems with reinforcement learning.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Core placement optimization for multichip many-core neural network systems with reinforcement learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:29.366256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9e9c4155-06d2-4889-9499-b9ad2091939a · outbound

This paper cites Towards spike-based machine intelligence with neuromorphic computing.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Towards spike-based machine intelligence with neuromorphic computing

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T10:15:28.381909Z

Source-reported events for the cited work

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Observation b7f2e563-0241-42e4-baf8-3f481b63f0a1 · outbound

This paper cites A wafer-scale neuromorphic hardware system for large-scale neural modeling.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training A wafer-scale neuromorphic hardware system for large-scale neural modeling

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:29.324729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8370491c-724e-4453-a258-47adfa690646 · outbound

This paper cites A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128k synapses.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128k synapses

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:29.295534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2b4d310c-aa08-4279-aa04-0d70db30c4fb · outbound

This paper cites A 0.086-mm ^212.7- pj/sop 64k-synapse 256-neuron online-learning digital spiking neuromorphic processor in 28-nm cmos.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training A 0.086-mm ^212.7- pj/sop 64k-synapse 256-neuron online-learning digital spiking neuromorphic processor in 28-nm cmos

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:29.270067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1879a77c-93ce-43e9-9d3e-5fc624baf81e · outbound

This paper cites Loihi: A neuromorphic manycore processor with on-chip learning.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Loihi: A neuromorphic manycore processor with on-chip learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:29.243356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 53b44ddc-ca45-421c-8fdf-3952080e99d2 · outbound

This paper cites H2learn: High-efficiency learning accelerator for high-accuracy spiking neural networks.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training H2learn: High-efficiency learning accelerator for high-accuracy spiking neural networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:29.222350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e8c8577d-9d49-4e10-8ef5-1ceef7c310e4 · outbound

This paper cites Tianjic: A unified and scalable chip bridging spike-based and continuous neural computation[J].

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Tianjic: A unified and scalable chip bridging spike-based and continuous neural computation[J]

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:29.193074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 03059b8a-7bfa-4c59-a205-3d2a592d051e · outbound

This paper cites Towards artificial general intelligence with hybrid tianjic chip architecture.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Towards artificial general intelligence with hybrid tianjic chip architecture

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:29.166980Z

Source-reported events for the cited work

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Observation eecf0057-24a7-4476-96df-2b85e7813e02 · outbound

This paper cites Policy gradient-based core placement optimization for multichip many-core systems.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Policy gradient-based core placement optimization for multichip many-core systems

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:29.142663Z

Source-reported events for the cited work

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Observation 7a2fdbc0-228c-4fdc-9959-d0c8250d6574 · outbound

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

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks

Reference 17

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Observation 2258029f-f830-4b10-89ec-47855ff947eb · outbound

This paper cites Understanding reuse, performance, and hardware cost of dnn dataflow: A data-centric approach.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Understanding reuse, performance, and hardware cost of dnn dataflow: A data-centric approach

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:29.096294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1eaa9bfa-a55b-464f-a0e8-562f99801ee6 · outbound

This paper cites Timeloop: A systematic approach to dnn accelerator evaluation.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Timeloop: A systematic approach to dnn accelerator evaluation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:29.074201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5f6aff42-180a-42ed-b6ef-b330d5f7398e · outbound

This paper cites Interstellar: Using Halide's Scheduling Language to Analyze DNN Accelerators.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Interstellar: Using Halide's Scheduling Language to Analyze DNN Accelerators

Reference 20

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Observation c918d4f5-e9e4-4416-bad2-872e11ada495 · outbound

This paper cites Heterogeneous fpga-based cost-optimal design for timing-constrained cnns.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Heterogeneous fpga-based cost-optimal design for timing-constrained cnns

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:29.044498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bbb9a07b-129c-4e89-af6b-3c5d19fd27e7 · outbound

This paper cites Xfer: A novel design to achieve super-linear performance on multiple fpgas for real-time ai.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Xfer: A novel design to achieve super-linear performance on multiple fpgas for real-time ai

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:29.021024Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation eeeb0af6-2846-4123-825b-8a45bc476189 · outbound

This paper cites A high performance fpga-based accelerator for large-scale convolutional neural networks.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training A high performance fpga-based accelerator for large-scale convolutional neural networks

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:28.998776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 44537e7c-e31d-4faa-849b-ac35761129ec · outbound

This paper cites Confuciux: Autonomous hardware resource assignment for dnn accelerators using reinforcement learning.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Confuciux: Autonomous hardware resource assignment for dnn accelerators using reinforcement learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:28.980881Z

Source-reported events for the cited work

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Observation 1c210780-d880-4b09-9811-2fe063a64d89 · outbound

This paper cites Fpdeep: Scalable acceleration of cnn training on deeply-pipelined fpga clusters.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Fpdeep: Scalable acceleration of cnn training on deeply-pipelined fpga clusters

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:28.959998Z

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Observation 4747260f-8481-455b-b67c-aea8fcc09f35 · outbound

This paper cites Flexlearn: fast and highly efficient brain simulations using flexible on-chip learning.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Flexlearn: fast and highly efficient brain simulations using flexible on-chip learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:28.939857Z

Source-reported events for the cited work

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Observation 349ca65a-95eb-4f3a-8a52-dbb3d824bb95 · outbound

This paper cites Gpipe: Efficient training of giant neural networks using pipeline parallelism.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Gpipe: Efficient training of giant neural networks using pipeline parallelism

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:28.919026Z

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Observation cd3dde52-7b2a-409d-aa56-34e5ba7b7931 · outbound

This paper cites Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

Reference 28

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Observation 3f7c494d-875b-46ed-aa7c-a7981237500b · outbound

This paper cites Deep learning with limited numerical precision.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Deep learning with limited numerical precision

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:28.900080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6f527fed-e2dd-4a0c-9824-b135fb4bcca7 · outbound

This paper cites Split learning for health: Distributed deep learning without sharing raw patient data.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Split learning for health: Distributed deep learning without sharing raw patient data

Reference 30

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Observation ca184443-abba-43fa-ade9-0f56e1f5192c · outbound

This paper cites Prime: A novel processing-in-memory architecture for neural network computation in reram-based main memory.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Prime: A novel processing-in-memory architecture for neural network computation in reram-based main memory

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:28.880498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e690eec8-38cf-4d49-bce4-17a4e11e9365 · outbound

This paper cites Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:28.862284Z

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Observation 0641f5d0-03e5-4470-b8e7-9a5f6a7d109b · outbound

This paper cites Semimap: A semifolded convolution mapping for speed-overhead balance on crossbars.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Semimap: A semifolded convolution mapping for speed-overhead balance on crossbars

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:28.844519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bf1e2282-36ec-4b54-b2d8-6b098e13162d · outbound

This paper cites Rql: Global placement via relaxed quadratic spreading and linearization.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Rql: Global placement via relaxed quadratic spreading and linearization

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:28.825944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5dbc864e-4556-4894-9cf6-666b78972865 · outbound

This paper cites Reinforcement learning for combinatorial optimization: A survey.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Reinforcement learning for combinatorial optimization: A survey

Reference 35

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Source-reported events for the cited work

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Observation 9c1d9268-3f80-40ec-bb4a-cff4bb36f0d7 · outbound

This paper cites Placement in Integrated Circuits using Cyclic Reinforcement Learning and Simulated Annealing.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Placement in Integrated Circuits using Cyclic Reinforcement Learning and Simulated Annealing

Reference 36

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Source-reported events for the cited work

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Observation 4c5642e1-21ac-4bd7-9346-e2a650ec2656 · outbound

This paper cites Fr¨oning.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Fr¨oning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:28.794289Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c513e5f7-98ae-4928-ab29-e9ddc2160925 · outbound

This paper cites Device placement optimization with reinforcement learning.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Device placement optimization with reinforcement learning

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:28.777459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 179a7ff6-bc95-48d4-8f98-5d442de35dd1 · outbound

This paper cites Spotlight: Optimizing device placement for training deep neural networks.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Spotlight: Optimizing device placement for training deep neural networks

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:28.759720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c632c36e-e2ae-403d-a0cf-efa70dd8b4d3 · outbound

This paper cites Post: Device placement with cross-entropy minimization and proximal policy optimization.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Post: Device placement with cross-entropy minimization and proximal policy optimization

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:28.741131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1d307251-cd21-4e93-8407-7153bcaa7b5a · outbound

This paper cites Baechi: fast device placement of machine learning graphs.

Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training Baechi: fast device placement of machine learning graphs

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:15:28.722286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

No inbound Pith citation observations are available.