Paper Citation Record · LEDGER

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI

As of 19 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2412.17966.

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

pith.paper-citation-record.v1
2412.17966 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:14:04.163730Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

22 of 22 outbound references displayed

verified exact0
verified fuzzy16
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4e0db2fe-ebe4-4a62-967c-2909d1e7b7d9 · outbound

This paper cites Exploiting correlation in stochastic circuit design,.

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI Exploiting correlation in stochastic circuit design,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:14:04.557955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7ad0d223-ed23-4eb6-bf75-9e33956938fe · outbound

This paper cites Akida NSoC,.

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI Akida NSoC,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:14:04.542695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation da67ba15-4b5e-48ab-9738-589569ef6b50 · outbound

This paper cites Taking ai to the edge: Google’s tpu now comes in a maker- friendly package,.

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI Taking ai to the edge: Google’s tpu now comes in a maker- friendly package,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:14:04.526916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7074ac6d-a7b0-446e-8544-b513163e8462 · outbound

This paper cites Nvidia’s xavier soc,.

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI Nvidia’s xavier soc,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:14:04.509728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6d5bd806-fae0-44e3-94a2-b03a0f2073b0 · outbound

This paper cites Ai benchmark: Running deep neural networks on android smartphones,.

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI Ai benchmark: Running deep neural networks on android smartphones,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:14:04.492072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e9316034-8366-4544-b904-4b7669e41019 · outbound

This paper cites In-datacenter performance analysis of a tensor processing unit,.

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI In-datacenter performance analysis of a tensor processing unit,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T05:14:04.079776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8a695b4-7999-47a0-9b30-aa3053458a70 · outbound

This paper cites Correlation manipulating circuits for stochastic computing,.

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI Correlation manipulating circuits for stochastic computing,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:14:04.462455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 513eecb7-2288-4b36-b71b-f4f0be896d7a · outbound

This paper cites Edge ai: On-demand accelerating deep neural network inference via edge computing,.

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI Edge ai: On-demand accelerating deep neural network inference via edge computing,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:14:04.446021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 356684da-e2c6-4b88-a8e9-bac3db91dff7 · outbound

This paper cites Energy efficient stochastic computing with sobol sequences,.

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI Energy efficient stochastic computing with sobol sequences,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:14:04.428855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation eb8e100d-c0f3-4d1e-8e63-dd8af15102a9 · outbound

This paper cites Systolic tensor array: An efficient structured-sparse gemm accelerator for mobile cnn inference,.

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI Systolic tensor array: An efficient structured-sparse gemm accelerator for mobile cnn inference,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:14:04.411014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 78d9f7a2-0b58-4158-b858-c3a9ed857d53 · outbound

This paper cites Mixed Low-precision Deep Learning Inference using Dynamic Fixed Point.

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI Mixed Low-precision Deep Learning Inference using Dynamic Fixed Point

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T05:14:04.103753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1ad2efe-4a37-493a-8e70-0432a8a92984 · outbound

This paper cites Convolutional Neural Networks using Logarithmic Data Representation.

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI Convolutional Neural Networks using Logarithmic Data Representation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T05:14:04.109947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efee6538-dad2-4ae9-bd74-5c499847f496 · outbound

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

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI A microarchitecture implementation framework for online learning with temporal neural networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:14:04.393704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 16439c9e-ff1d-4529-9748-8bc95b2686ce · outbound

This paper cites CLBlast,.

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI CLBlast,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:14:04.375895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e3b3cc46-7d27-4710-bf2a-a92dbf2485e5 · outbound

This paper cites an unresolved cited work.

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-11T05:14:04.359341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 100c588a-b371-4c79-97b4-36848946d41d · outbound

This paper cites An approximate gemm unit for energy-efficient object detection,.

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI An approximate gemm unit for energy-efficient object detection,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:14:04.343028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c5420a91-c8d9-41d2-80b0-f0cf0680ef6b · outbound

This paper cites Edge computing: Vision and challenges,.

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI Edge computing: Vision and challenges,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:14:04.325528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e1e7aaab-c16a-4534-a2f5-ad64bd2478ce · outbound

This paper cites Ultra- low precision 4-bit training of deep neural networks,.

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI Ultra- low precision 4-bit training of deep neural networks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:14:04.309264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 29f0f6e7-406e-4840-8187-797290de0b93 · outbound

This paper cites The Computational Limits of Deep Learning.

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI The Computational Limits of Deep Learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T05:14:04.148516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90a27ed2-452d-4f53-ae0a-87a6f3980178 · outbound

This paper cites 8-bit precision for training deep learning systems,.

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI 8-bit precision for training deep learning systems,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T05:14:04.154044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ceb4260-44a4-4285-97c3-5227d0df4793 · outbound

This paper cites Ugemm: Unary computing architecture for gemm applications,.

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI Ugemm: Unary computing architecture for gemm applications,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:14:04.280924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4e24976c-e5c1-4553-bb46-0fa41376bd61 · outbound

This paper cites Demystifying tensor cores to optimize half-precision matrix multiply,.

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI Demystifying tensor cores to optimize half-precision matrix multiply,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:14:04.262011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.