Paper Citation Record · LEDGER

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation

As of 11 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2501.01841.

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

pith.paper-citation-record.v1
2501.01841 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:26:32.657304Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

32 of 32 outbound references displayed

verified exact6
verified fuzzy8
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5e789f1c-a4d9-4967-9ad4-1be227511bec · outbound

This paper cites A 7nm 4-core AI chip with 25.6TFLOPS hybrid FP8 train- ing, 102.4TOPS INT4 inference and workload-aware throt- tling.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation A 7nm 4-core AI chip with 25.6TFLOPS hybrid FP8 train- ing, 102.4TOPS INT4 inference and workload-aware throt- tling

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:26:33.341766Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 365e6472-1de2-4944-9675-74414df8976c · outbound

This paper cites LSQ+: Improving low-bit quantization through learnable offsets and better initialization.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation LSQ+: Improving low-bit quantization through learnable offsets and better initialization

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T22:26:32.469983Z

Source-reported events for the cited work

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Observation 865a77ac-b80b-48d9-97ed-7330525e6068 · outbound

This paper cites YOLACT: Real-time Instance Segmentation.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation YOLACT: Real-time Instance Segmentation

Reference 3

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Source-reported events for the cited work

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Observation df511998-a0eb-4762-a2fd-cc7f756432e7 · outbound

This paper cites Deep Learning with Low Precision by Half-wave Gaussian Quantization.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation Deep Learning with Low Precision by Half-wave Gaussian Quantization

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T22:26:32.481541Z

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Observation 51fbd198-6978-4364-830f-1087c84f9435 · outbound

This paper cites Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T22:26:32.486662Z

Source-reported events for the cited work

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Observation 76a69b29-a8fe-44c7-afc5-476696b65483 · outbound

This paper cites Condensation-Net: memory-efﬁcient network architecture with cross-channel pooling layers and virtual feature maps ,.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation Condensation-Net: memory-efﬁcient network architecture with cross-channel pooling layers and virtual feature maps ,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:26:33.324376Z

Source-reported events for the cited work

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Observation 53f62a42-b130-40ee-a4d6-8709ac04d7cc · outbound

This paper cites Sparse Instance Activation for Real-Time Instance Segmentation.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation Sparse Instance Activation for Real-Time Instance Segmentation

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:26:33.062903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6ee5b13a-a2bc-498f-ba90-c6630af413e7 · outbound

This paper cites PACT: Parameterized Clipping Activation for Quantized Neural Networks.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation PACT: Parameterized Clipping Activation for Quantized Neural Networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T22:26:32.507849Z

Source-reported events for the cited work

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Observation ec748108-3985-41ab-a97c-dd18ec2da31e · outbound

This paper cites A binary weight convolutional neural network hardware accelerator for analysis faults of the CNC machinery on FPGA.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation A binary weight convolutional neural network hardware accelerator for analysis faults of the CNC machinery on FPGA

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:26:33.307858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 82f7d634-dfc7-4a2c-9d3e-b66042f21f5d · outbound

This paper cites BinaryConnect: Training Deep Neural Networks with binary weights during propagations.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation BinaryConnect: Training Deep Neural Networks with binary weights during propagations

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T22:26:32.525357Z

Source-reported events for the cited work

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Observation 3b95762b-7e79-4198-a2ed-16e10d022e60 · outbound

This paper cites Semantic Image Segmentation: Two Decades of Research.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation Semantic Image Segmentation: Two Decades of Research

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:26:32.992758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e0fc507c-effa-4a73-9c9c-08cc405e4f01 · outbound

This paper cites RetinaFace: Single-stage Dense Face Localisation in the Wild.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation RetinaFace: Single-stage Dense Face Localisation in the Wild

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T22:26:32.540155Z

Source-reported events for the cited work

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Observation 9bcd83e9-39a9-4969-82a7-f04886d171f4 · outbound

This paper cites Learned Step Size Quantization.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation Learned Step Size Quantization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T22:26:32.547329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 269cdb30-3442-4118-98c0-94da31e5b1f8 · outbound

This paper cites IFQ-Net: Integrated Fixed-point Quantization Networks for Embedded Vision.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation IFQ-Net: Integrated Fixed-point Quantization Networks for Embedded Vision

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:26:32.921955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 20d09003-c57a-4cbf-ae75-589a47ab453a · outbound

This paper cites Efﬁcient binary weight convolu- tional network accelerator for speech recognition.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation Efﬁcient binary weight convolu- tional network accelerator for speech recognition

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:26:33.289573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 001fc0c4-4cc8-4a9c-8668-44ed5c02ae10 · outbound

This paper cites SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

Reference 16

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Source-reported events for the cited work

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Observation 42056eb9-328a-4a16-b79f-72a1432c049e · outbound

This paper cites A high-efﬁciency FPGA-based accelerator for binarized neu- ral network.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation A high-efﬁciency FPGA-based accelerator for binarized neu- ral network

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:26:33.269897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 920cc427-e267-4224-9434-33d060286e5f · outbound

This paper cites BitFlow: Exploit- ing vector parallelism for binary neural networks on CPU.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation BitFlow: Exploit- ing vector parallelism for binary neural networks on CPU

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:26:33.249880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bd610ddf-efa5-427d-80ae-5e852ae070c5 · outbound

This paper cites Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural Networks.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural Networks

Reference 19

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T22:26:32.868070Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0eff7b36-891b-489f-97ab-02454578bc63 · outbound

This paper cites Microsoft COCO: Common Objects in Context.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation Microsoft COCO: Common Objects in Context

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T22:26:32.601388Z

Source-reported events for the cited work

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Observation c6f9a955-d832-4844-a33c-3f55d1c6cca4 · outbound

This paper cites PROFIT: A Novel Training Method for sub-4-bit MobileNet Models.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation PROFIT: A Novel Training Method for sub-4-bit MobileNet Models

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:26:32.822508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 359c13dc-be76-4a49-a901-a2428a74f268 · outbound

This paper cites Binary Neural Networks: A Survey.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation Binary Neural Networks: A Survey

Reference 22

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Source-reported events for the cited work

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Observation 44d3c6f7-36d8-493d-8abf-d13eb0b444b5 · outbound

This paper cites Vision Transformers for Dense Prediction.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation Vision Transformers for Dense Prediction

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T22:26:32.615497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c96d4d1b-e0c7-454b-88c4-71aaa51be10e · outbound

This paper cites XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T22:26:32.619754Z

Source-reported events for the cited work

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Observation a004891d-f2fe-4c48-9211-b2ec39cd0109 · outbound

This paper cites A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T22:26:32.624175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b2a31ba-b31e-4e2d-a97e-0028566701fb · outbound

This paper cites A CNN accelerator on FPGA using binary weight networks.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation A CNN accelerator on FPGA using binary weight networks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:26:33.232622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c9b19584-2d10-4011-b007-e9acd896fac0 · outbound

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Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:26:33.216717Z

Source-reported events for the cited work

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Observation cd65c733-f424-4f0b-8de9-0907dbd09c4b · outbound

This paper cites ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T22:26:32.640059Z

Source-reported events for the cited work

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Observation aac8bc7f-3c52-40d6-b8fc-68076999a75f · outbound

This paper cites ReCU: Reviving the Dead Weights in Binary Neural Networks.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation ReCU: Reviving the Dead Weights in Binary Neural Networks

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:26:32.712581Z

Source-reported events for the cited work

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Observation 1c1bada2-2149-48b9-9242-a55893bbe682 · outbound

This paper cites On-chip mem- ory based binarized convolutional deep neural network ap- plying batch normalization free technique on an FPGA.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation On-chip mem- ory based binarized convolutional deep neural network ap- plying batch normalization free technique on an FPGA

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:26:33.199947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3cbefcbf-e2c2-4b9f-bced-59d2fbec5078 · outbound

This paper cites an unresolved cited work.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:26:33.180773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 14a80758-6be1-42c1-ba67-75ac50131d00 · outbound

This paper cites Condensation-Net: Memory-Efficient Network Architecture with Cross-Channel Pooling Layers and Virtual Feature Maps.

Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation Condensation-Net: Memory-Efficient Network Architecture with Cross-Channel Pooling Layers and Virtual Feature Maps

Reference 2021

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:26:33.089259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.