Paper Citation Record · LEDGER

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators

As of 20 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2508.21524.

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

pith.paper-citation-record.v1
2508.21524 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:47:31.624273Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

24 of 24 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7dbb9abb-f045-47e4-ba4c-df90492862bb · outbound

This paper cites Deep Residual Learning for Image Recognition,.

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators Deep Residual Learning for Image Recognition,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:32.085011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 14972a5c-75b6-42c6-9a27-682ab59d2ec0 · outbound

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

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators Very Deep Convolutional Networks for Large-Scale Image Recognition,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:32.069928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2167212e-7439-4951-bb0d-5678fcf84696 · outbound

This paper cites ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars,.

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:32.055018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 89b2db90-8953-4135-aa8f-d064005e1c68 · outbound

This paper cites IMCE: Energy-Efficient Bit-Wise In-Memory Convo- lution Engine for Deep Neural Network,.

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators IMCE: Energy-Efficient Bit-Wise In-Memory Convo- lution Engine for Deep Neural Network,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:32.039563Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation db2a3623-7203-471d-a083-2123b676945c · outbound

This paper cites Towards Robust RRAM-Based Vision Transformer Models with Noise-Aware Knowledge Distillation,.

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators Towards Robust RRAM-Based Vision Transformer Models with Noise-Aware Knowledge Distillation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:31.966626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bfdd84e4-cc25-4b82-bcfa-970c3959adf8 · outbound

This paper cites RRAM-Based Isotropic CNNs with High Robustness and Resource Utilization Rate,.

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators RRAM-Based Isotropic CNNs with High Robustness and Resource Utilization Rate,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:31.950907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 152bd90e-d0c0-4301-98c8-4446c87b5f39 · outbound

This paper cites DNN+NeuroSim V2.0: An End-to-End Benchmarking Framework for Compute-in-Memory Accelerators for On-Chip Train- ing,.

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators DNN+NeuroSim V2.0: An End-to-End Benchmarking Framework for Compute-in-Memory Accelerators for On-Chip Train- ing,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:31.936338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 958b4442-40c7-453b-b5d7-24ed21ee0fd1 · outbound

This paper cites ReactNet: Towards Precise Binary Neural Network with Generalized Activation Functions,.

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators ReactNet: Towards Precise Binary Neural Network with Generalized Activation Functions,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:31.921171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5c9a2244-b16b-47ea-9adb-8204ddfcb0c8 · outbound

This paper cites How To Train A Compact Binary Neural Network with High Accuracy?,.

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators How To Train A Compact Binary Neural Network with High Accuracy?,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:31.906062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e9bc7175-30b7-4bff-8472-23f117983be3 · outbound

This paper cites Mixed Precision Quantization for ReRAM-based DNN Inference Accelerators,.

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators Mixed Precision Quantization for ReRAM-based DNN Inference Accelerators,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:31.890261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8c858567-277e-4a0f-960f-a2180b0775b6 · outbound

This paper cites Q-PIM: A Genetic Algorithm Based Flexible DNN Quantization Method and Application to Processing-in-Memory Plat- form,.

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators Q-PIM: A Genetic Algorithm Based Flexible DNN Quantization Method and Application to Processing-in-Memory Plat- form,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:31.873798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1b74084c-ef55-4a33-ae14-a77b4b9e0f4b · outbound

This paper cites Genetic Algorithm-Based Energy-Aware CNN Quan- tization for Processing-In-Memory Architecture,.

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators Genetic Algorithm-Based Energy-Aware CNN Quan- tization for Processing-In-Memory Architecture,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:31.858255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ef57a4dd-6b0e-4987-afcc-c1f959c3d2d5 · outbound

This paper cites XNOR-RRAM: A Scalable and Parallel Resistive Synaptic Architecture for Binary Neural Networks,.

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators XNOR-RRAM: A Scalable and Parallel Resistive Synaptic Architecture for Binary Neural Networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:31.842731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 85004790-7d38-4d69-9fc7-e8efc4a90b20 · outbound

This paper cites CiM-BNN: Computing-in-MRAM Architecture for Stochastic Computing Based Bayesian Neural Network,.

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators CiM-BNN: Computing-in-MRAM Architecture for Stochastic Computing Based Bayesian Neural Network,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:31.827108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 65c1735d-5fce-4a4b-8b8b-4a5184c89b59 · outbound

This paper cites CIMQ: A Hardware-efficient Quantization Framework for Computing-In-Memory-Based Neural Network Accelerators,.

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators CIMQ: A Hardware-efficient Quantization Framework for Computing-In-Memory-Based Neural Network Accelerators,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:31.810932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5c1b22ae-0a56-45cf-a0a7-3d281cc570cc · outbound

This paper cites FINN: A Framework for Fast, Scalable Binarized Neural Network Inference,.

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators FINN: A Framework for Fast, Scalable Binarized Neural Network Inference,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:31.794630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0216b5ea-9740-4510-87c8-b027eb128193 · outbound

This paper cites Training Binary Neural Networks with Real-to- Binary Convolutions,.

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators Training Binary Neural Networks with Real-to- Binary Convolutions,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:31.777794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cbb50a82-f2d1-495b-8f20-5a7f9f190cfe · outbound

This paper cites Balanced Binary Neural Networks with Gated Resid- ual,.

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators Balanced Binary Neural Networks with Gated Resid- ual,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:31.761176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 79690ef3-7a1e-4352-86e0-4080eeac0781 · outbound

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

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators PACT: Parameterized Clipping Activation for Quantized Neural Networks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:31.744524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2e7b5b1c-8746-4139-b309-6335eec1214c · outbound

This paper cites Adabin: Improving Binary Neural Networks with Adaptive Binary Sets,.

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators Adabin: Improving Binary Neural Networks with Adaptive Binary Sets,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:31.729052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a436654b-1bac-45e0-9d1f-0e144ce7691d · outbound

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

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Reference 21

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Source-reported events for the cited work

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Observation b2a844c0-5f0a-48af-ab58-23bc5d3087c5 · outbound

This paper cites Bi-real Net: Enhancing the Performance of 1-bit CNNs with Improved Representational Capability and Advanced Training Algorithm,.

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators Bi-real Net: Enhancing the Performance of 1-bit CNNs with Improved Representational Capability and Advanced Training Algorithm,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:31.713787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5be358e2-6c65-4710-8d27-dc265db72963 · outbound

This paper cites BinaryDenseNet: Developing an Architecture for Binary Neural Networks,.

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators BinaryDenseNet: Developing an Architecture for Binary Neural Networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:31.696808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f6649f86-ab2a-4ffd-a6c8-40dcc53b67cc · outbound

This paper cites MeliusNet: An Improved Network Architecture for Binary Neural Networks,.

Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators MeliusNet: An Improved Network Architecture for Binary Neural Networks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:47:31.679743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.