Pith. sign in

Paper Citation Record · LEDGER

MLPerf Tiny Benchmark

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2106.07597.

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

pith.paper-citation-record.v1
2106.07597 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 20 of 20 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:42:21.976648Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T02:55:53.494035Z

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 ff09f248-ddb9-4699-887d-6d1adbe8f715 · inbound

Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications cites this paper.

Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications MLPerf Tiny Benchmark

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-24T01:08:41.928544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-24T01:06:48.298874Z digest=sha256:40c95b17b50df2da95f201c25cadd7954ec8a1a3c8360f7ab3579fdd8d268e3e

Observation 7b458b84-d54a-445c-ad59-ff34b1139623 · inbound

Searching Neural Architectures for Sensor Nodes on IoT Gateways cites this paper.

Searching Neural Architectures for Sensor Nodes on IoT Gateways MLPerf Tiny Benchmark

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-07T12:42:21.976648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:42:21.976648Z digest=sha256:6542231c242ee5ec843e699f7f593bc783ac589066f4a957c0af8c958e8ce740

Observation 534fb257-e080-436e-a9ed-1bdc4cb56d51 · inbound

Tensor Program Optimization for the RISC-V Vector Extension Using Probabilistic Programs cites this paper.

Tensor Program Optimization for the RISC-V Vector Extension Using Probabilistic Programs MLPerf Tiny Benchmark

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T20:55:37.780009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:55:37.780009Z digest=sha256:4d222358ab567ba35f46861188df2a320a5d552e000ac6733bbe482ce7b44064

Observation 0eae516d-ecaf-488a-8016-aec2bb04a828 · inbound

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications cites this paper.

Hardware-efficient tractable probabilistic inference for TinyML Neurosymbolic AI applications MLPerf Tiny Benchmark

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T19:40:20.549122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:40:20.549122Z digest=sha256:d54ac0ad8ff8765fb1f110617179539d987d798b45b6f7d65da97d185bd7620c

Observation b3b818d6-0b18-4bbb-9fa2-e790c5b42821 · inbound

Flexible Vector Integration in Embedded RISC-V SoCs for End to End CNN Inference Acceleration cites this paper.

Flexible Vector Integration in Embedded RISC-V SoCs for End to End CNN Inference Acceleration MLPerf Tiny Benchmark

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T16:09:32.933918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:09:32.933918Z digest=sha256:e56a0e059ba8b23939b1f90426fa357f67208b1cb98e7332c4423fadccbfb0c6

Observation 5d4734d2-6541-4c7e-82c9-f425f9595bdc · inbound

Real-Time Performance Benchmarking of TinyML Models in Embedded Systems (PICO: Performance of Inference, CPU, and Operations) cites this paper.

Real-Time Performance Benchmarking of TinyML Models in Embedded Systems (PICO: Performance of Inference, CPU, and Operations) MLPerf Tiny Benchmark

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-05T05:59:51.067059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:59:51.067059Z digest=sha256:e4c6c8991b3689ea930813826c54252a4119fea5a41ed7d7810c3fd2984547d2

Observation d2b5002d-42f3-4246-9fd4-2b028f198400 · inbound

Ariel-ML: Computing Parallelization with Embedded Rust for Neural Networks on Heterogeneous Multi-core Microcontrollers cites this paper.

Ariel-ML: Computing Parallelization with Embedded Rust for Neural Networks on Heterogeneous Multi-core Microcontrollers MLPerf Tiny Benchmark

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-03T17:24:26.197603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T17:24:26.197603Z digest=sha256:bf952d610964b7c44dd35024900f363a1ec36922a5abc2697a0b162b03cbe9e0

Observation ae6129ac-7bc0-4663-9604-3ef07bcfc7fb · inbound

Design Rules for Extreme-Edge Scientific Computing on AI Engines cites this paper.

Design Rules for Extreme-Edge Scientific Computing on AI Engines MLPerf Tiny Benchmark

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-05-11T13:06:05.521545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T02:19:21.143588Z digest=sha256:48d3c68ce4394a95bec0fc60306eaea6da08fe31ba7eb1be48e7f816062d6bc0

Observation 686ac15f-a227-4666-99b7-0cf926a16c67 · inbound

Are Large Language Models Economically Viable for Industry Deployment? cites this paper.

Are Large Language Models Economically Viable for Industry Deployment? MLPerf Tiny Benchmark

Reference 51

Resolution
verified exact
arxiv_id, observed 2026-05-11T13:01:20.353349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-10T02:28:12.686424Z digest=sha256:e9038be844ba967603c71bf28c3b9c323f19ea5d35b7e131b9db3ac6b15c6a16

Observation fb75ef33-d277-4386-b014-85c06a7565cb · inbound

A Fully Tunable Ultra-Low Power Current-Mode Memory Cell in Standard CMOS Technology cites this paper.

A Fully Tunable Ultra-Low Power Current-Mode Memory Cell in Standard CMOS Technology MLPerf Tiny Benchmark

Reference 49

Resolution
verified exact
arxiv_id, observed 2026-05-11T03:20:56.108853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-11T03:16:27.371359Z digest=sha256:33f009809c9ddc286e9c79f039962266e75d46f9c3685a7e22b29258bd5e2f21

Observation 88cbba5b-de60-4451-acdd-89405e8338b7 · inbound

A Fully Tunable Ultra-Low Power Current-Mode Memory Cell in Standard CMOS Technology cites this paper.

A Fully Tunable Ultra-Low Power Current-Mode Memory Cell in Standard CMOS Technology MLPerf Tiny Benchmark

Reference 49

Resolution
verified exact
arxiv_id, observed 2026-05-20T22:49:10.376572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-20T22:46:56.512703Z digest=sha256:d3255eb98babc8357935a36e35bf44f8b44a73b963b2961366da86d481f3bceb

Observation 68b7077c-c1f3-4d8d-87d1-0a3c0484e447 · inbound

QuIDE: Mastering the Quantized Intelligence Trade-off via Active Optimization cites this paper.

QuIDE: Mastering the Quantized Intelligence Trade-off via Active Optimization MLPerf Tiny Benchmark

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-13T07:52:32.341381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-13T07:47:38.066716Z digest=sha256:ebd853bf23e3c557b0a9bb2a67842576b0575c60e8f9a1bd16b1592decf127be

Observation 43fef4f5-0840-4577-9813-ea80c5f7ec15 · inbound

Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations cites this paper.

Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations MLPerf Tiny Benchmark

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-20T22:09:07.264633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-20T22:07:34.292536Z digest=sha256:6437edea40939b784808871225107991508a6a1533804adbc4cc5c9e49160411

Observation ff70bbb9-4f2d-4ba2-b20d-e173899b1c71 · inbound

Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations cites this paper.

Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations MLPerf Tiny Benchmark

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-07-01T14:05:46.340205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-30T22:21:16.608148Z digest=sha256:aa9b7af302b39ca6f1f31befcd48116649ede3c8f9cf6afad947e693b864007b

Observation d512dca2-2df3-4f78-99be-17008ae256da · inbound

Perforated Neural Networks for Keyword Spotting cites this paper.

Perforated Neural Networks for Keyword Spotting MLPerf Tiny Benchmark

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-20T21:13:44.818066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-20T21:10:09.974162Z digest=sha256:bfa03460ee3def2542a5bc581485c06e317d5333e3df5710db008ee520f76b32

Observation 28373b91-2567-4615-ae97-d2e682ab8820 · inbound

OpenGlass: Ultra-Low-Power On-Device AI Eyewear with Event-based Vision cites this paper.

OpenGlass: Ultra-Low-Power On-Device AI Eyewear with Event-based Vision MLPerf Tiny Benchmark

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-07-02T17:27:15.080350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-27T22:03:18.602151Z digest=sha256:e31630a6c5f507698fb91c9e556859fad734d7d7ceabe49b431a1bcb3b8e7e6f

Observation 2fa0aaf9-1cb5-45be-9346-41cfdfe39fde · inbound

Breaking TinyML: Why Quantized Neural Networks Need Domain-Specific Security Analysis cites this paper.

Breaking TinyML: Why Quantized Neural Networks Need Domain-Specific Security Analysis MLPerf Tiny Benchmark

Reference 9

Resolution
unresolved
no resolver link, observed 2026-07-12T14:06:47.857777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T14:06:47.857777Z digest=sha256:6dfd2aae037d1735707d8222498b2104332038691a40eb36005318cd9b634393

Observation d03f2752-f7a4-47e6-86b2-c7d6c476e2aa · inbound

Efficient Network Inference via Hardware-Aware Architecture Search, Model Pruning & Quantization cites this paper.

Efficient Network Inference via Hardware-Aware Architecture Search, Model Pruning & Quantization MLPerf Tiny Benchmark

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-07-04T09:49:45.040981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-26T09:21:01.698209Z digest=sha256:845ac56ae05b478ae4b5306b9a679bdf721de95b20f3b30429d15cc0e8786705

Observation 57eff531-f8b7-415f-a30f-9623b599e7f1 · inbound

AdvScan: Black-Box Adversarial Example Detection at Runtime through Power Analysis cites this paper.

AdvScan: Black-Box Adversarial Example Detection at Runtime through Power Analysis MLPerf Tiny Benchmark

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-07-01T16:55:50.973127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T04:27:36.458570Z digest=sha256:1ab9c71e7eabf491ebe19611fed7de22521714797021a69b29f7b11a4620e36f

Observation c30300be-bc0c-489a-9b83-5af0a5ad5b2a · inbound

ECGLight: Compute-Light Framework For Paper ECG Digitization and Myocardial Infarction Screening cites this paper.

ECGLight: Compute-Light Framework For Paper ECG Digitization and Myocardial Infarction Screening MLPerf Tiny Benchmark

Reference 33

Resolution
metadata mismatch
local_arxiv, observed 2026-07-09T02:55:53.495667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-07-09T02:52:36.673562Z digest=sha256:ca643470939f476b2e6f8251bc4ead0b407228778df7e624f964053fc8de03bf