Paper Citation Record · LEDGER

Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2006.10159.

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

pith.paper-citation-record.v1
2006.10159 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:17:53.982061Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T02:37:34.104045Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation eabdc18e-98d9-4759-979f-eb75c98e00b1 · inbound

Neural Architecture Codesign for Fast Physics Applications cites this paper.

Neural Architecture Codesign for Fast Physics Applications Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T21:17:53.982061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e1fe0c6-f287-409b-ab56-086da681c3e3 · inbound

EdgeMark: An Automation and Benchmarking System for Embedded Artificial Intelligence Tools cites this paper.

EdgeMark: An Automation and Benchmarking System for Embedded Artificial Intelligence Tools Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-09T16:27:51.519223Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba7760ea-bc63-4e3c-a92a-9947485acdf9 · inbound

Arbitrary Precision Printed Ternary Neural Networks with Holistic Evolutionary Approximation cites this paper.

Arbitrary Precision Printed Ternary Neural Networks with Holistic Evolutionary Approximation Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T15:38:49.858003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9a5cd62-bf88-4675-b8da-eaf572dafb52 · inbound

wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation cites this paper.

wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T23:44:05.083321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9988462f-d4e0-41bd-b1e6-adab91f6fb58 · inbound

SparsePixels: Efficient Convolution for Sparse Data on FPGAs cites this paper.

SparsePixels: Efficient Convolution for Sparse Data on FPGAs Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T18:18:39.882716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5252f2e-7424-4903-b6de-91bc9da51e26 · inbound

On-chip probabilistic inference for charged-particle tracking at the sensor edge cites this paper.

On-chip probabilistic inference for charged-particle tracking at the sensor edge Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-15T21:40:21.170945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8a1dcf84-9568-4819-9733-d9a5f753cb7b · inbound

Real-time Surface-Code Error Correction Using an FPGA-based Neural-Network Decoder cites this paper.

Real-time Surface-Code Error Correction Using an FPGA-based Neural-Network Decoder Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors

Reference 73

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:06:05.902200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bbc855f6-9f74-4d5e-bd6d-b6f148c9d6ae · inbound

APEIRON: composing smart TDAQ systems for high energy physics experiments cites this paper.

APEIRON: composing smart TDAQ systems for high energy physics experiments Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T02:37:34.105716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.