Paper Citation Record · LEDGER

TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering

As of 19 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2412.17977.

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

pith.paper-citation-record.v1
2412.17977 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:10:54.672437Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

20 of 20 outbound references displayed

verified exact2
verified fuzzy14
unresolved4
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 26a2136b-0ace-432a-8427-e5c0131bcf01 · outbound

This paper cites Time-series clustering–a decade review,.

TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering Time-series clustering–a decade review,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:10:54.898828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cba795dc-e8b5-4d1f-a40a-6e73b382890b · outbound

This paper cites Unsupervised clustering of time series signals using neuromorphic energy-efficient temporal neural networks,.

TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering Unsupervised clustering of time series signals using neuromorphic energy-efficient temporal neural networks,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:10:54.888494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9090b491-7a4d-4eb6-b1bf-5ac25485ab49 · outbound

This paper cites Asap7: A 7-nm finfet predictive process design kit,.

TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering Asap7: A 7-nm finfet predictive process design kit,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:10:54.877963Z

Source-reported events for the cited work

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Observation f5400405-f3cd-41de-8706-34f8d32d86af · outbound

This paper cites The UCR time series classi- fication archive,.

TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering The UCR time series classi- fication archive,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:10:54.867473Z

Source-reported events for the cited work

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Observation 5c2c88c9-180e-476a-8491-a28cee2b4e2e · outbound

This paper cites Deep learning,.

TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering Deep learning,

Reference 5

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Source-reported events for the cited work

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Observation 9783db6b-4d14-4728-a732-739bd17c11de · outbound

This paper cites Learning representations for time series clustering,.

TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering Learning representations for time series clustering,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:10:54.850644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 868cf55d-f171-40f6-b256-8c471c99a3b2 · outbound

This paper cites A microarchitecture implementation framework for online learning with temporal neural networks,.

TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering A microarchitecture implementation framework for online learning with temporal neural networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:10:54.840050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ec96c558-c0e2-48a9-ad3b-a474bcca3a4b · outbound

This paper cites Tnn7: A custom macro suite for implementing highly optimized designs of neuromorphic tnns,.

TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering Tnn7: A custom macro suite for implementing highly optimized designs of neuromorphic tnns,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:10:54.829751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cc1394e7-5b4e-4575-8f37-4092c3cbd8bb · outbound

This paper cites (2022) Ai is harming our planet: addressing ai’s staggering energy cost.

TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering (2022) Ai is harming our planet: addressing ai’s staggering energy cost

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:10:54.819665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7d665d22-e1af-411d-a1c2-27414c022a01 · outbound

This paper cites Ascend-freepdk45: An open source standard cell library for asyn- chronous design,.

TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering Ascend-freepdk45: An open source standard cell library for asyn- chronous design,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:10:54.809374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a2d2a656-87c1-413d-a383-a100db699112 · outbound

This paper cites (2018) Ai and compute.

TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering (2018) Ai and compute

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:10:54.798336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b01e050a-d522-4260-afc7-e440bc40ad29 · outbound

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

TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering Pytorch: An imperative style, high-performance deep learning library,

Reference 12

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Source-reported events for the cited work

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Observation 9c723405-7e16-44c7-a8a2-3f577547327a · outbound

This paper cites Cortical columns computing systems: Microar- chitecture model, functional building blocks, and design tools,.

TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering Cortical columns computing systems: Microar- chitecture model, functional building blocks, and design tools,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:10:54.781512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 364daa57-d517-4c40-8929-0d381ecb5907 · outbound

This paper cites Space-time algebra: A model for neocortical computation,.

TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering Space-time algebra: A model for neocortical computation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:10:54.770785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a180b43e-bd74-4dbd-b3f5-4fd6888f2bf5 · outbound

This paper cites Space-time computing with temporal neural networks,.

TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering Space-time computing with temporal neural networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:10:54.758415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 59601937-1e49-4e2c-ba62-b1d35e43b1a8 · outbound

This paper cites A Temporal Neural Network Architecture for Online Learning.

TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering A Temporal Neural Network Architecture for Online Learning

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-11T05:10:54.736139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a378c423-426f-44ee-ac93-77e31ca195ac · outbound

This paper cites Pyverilog: A python-based hardware design processing toolkit for verilog hdl,.

TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering Pyverilog: A python-based hardware design processing toolkit for verilog hdl,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T05:10:54.660407Z

Source-reported events for the cited work

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Observation 582f4fcd-a122-4e44-8ff3-8aceabdad328 · outbound

This paper cites The Computational Limits of Deep Learning.

TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering The Computational Limits of Deep Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T05:10:54.663968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54be10b8-c289-430a-b810-d45f90b65aae · outbound

This paper cites Towards a Design Framework for TNN-Based Neuromorphic Sensory Processing Units.

TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering Towards a Design Framework for TNN-Based Neuromorphic Sensory Processing Units

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-11T05:10:54.713196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4edbea3f-4a08-4e63-8311-1e0d742e53c5 · outbound

This paper cites Salient subsequence learning for time series clustering,.

TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering Salient subsequence learning for time series clustering,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:10:54.746608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.