Paper Citation Record · LEDGER

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network

As of 19 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2503.21337.

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

pith.paper-citation-record.v1
2503.21337 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-22T23:37:04.178987Z

measured 33 of 33 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

33 of 33 outbound references displayed

verified exact4
verified fuzzy29
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c2dd04ee-80f0-400b-8bfe-3c4f22cfe250 · outbound

This paper cites Automatic speech recognition: systematic literature review.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network Automatic speech recognition: systematic literature review

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.573152Z

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 bcb71c51-f4ac-498d-9c4a-6a1d213992df · outbound

This paper cites A fully integrated 1.7mW attention-based automatic speech recognition processor.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network A fully integrated 1.7mW attention-based automatic speech recognition processor

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.577178Z

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 80765d9c-6fa7-44ff-abb6-dfe2a9a7ccdb · outbound

This paper cites An 8.93 TOPS/W LSTM recurrent neural network accelerator featuring hierarchical coarse-grain sparsity for on-device speech recognition.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network An 8.93 TOPS/W LSTM recurrent neural network accelerator featuring hierarchical coarse-grain sparsity for on-device speech recognition

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.611378Z

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 b5375f15-5695-4270-bcc9-1894d095c581 · outbound

This paper cites A 16-nm SoC for noise-robust speech and NLP edge AI inference with bayesian sound source separation and attention-based DNNs.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network A 16-nm SoC for noise-robust speech and NLP edge AI inference with bayesian sound source separation and attention-based DNNs

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.550598Z

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 82cb020e-1583-430a-b468-b9a1c4f13270 · outbound

This paper cites Attention-based models for speech recognition.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network Attention-based models for speech recognition

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.546079Z

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 e04376a3-0535-4c7e-b326-84e4adcd7528 · outbound

This paper cites Listen, attend and spell: a neural network for large vocabulary conversational speech recognition.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network Listen, attend and spell: a neural network for large vocabulary conversational speech recognition

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.518228Z

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 d10e3ddf-ed66-48b8-9e82-f0ad4baee5d6 · outbound

This paper cites Attention is all you need.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network Attention is all you need

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.513551Z

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 36b1b0c3-8ae8-4907-a936-03772b2cc00e · outbound

This paper cites Streaming automatic speech recognition with the transformer model.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network Streaming automatic speech recognition with the transformer model

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.623143Z

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 fcab8d0f-12ef-4034-8a4e-290e3db4a3c3 · outbound

This paper cites Interactive feature fusion for end-to-end noise-robust speech recognition.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network Interactive feature fusion for end-to-end noise-robust speech recognition

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.581487Z

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 a59de181-e2cf-4b21-996d-c267d039cad6 · outbound

This paper cites An ultra-low power binarized convolutional neural network-based speech recognition processor with on-chip self-learning.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network An ultra-low power binarized convolutional neural network-based speech recognition processor with on-chip self-learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.585826Z

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 48055234-67be-4a7b-a9dc-dd90a1b2dc1c · outbound

This paper cites Deep learning incorporating biologically inspired neural dynamics and in-memory computing.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network Deep learning incorporating biologically inspired neural dynamics and in-memory computing

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.634973Z

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 fc536ed4-26ec-498f-8fe7-87e8abe9a816 · outbound

This paper cites A tandem learning rule for effective training and rapid inference of deep spiking neural networks.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network A tandem learning rule for effective training and rapid inference of deep spiking neural networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.541670Z

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 69edf484-b261-4ca4-bdd7-968417fe5ac5 · outbound

This paper cites Input-Aware Dynamic Timestep Spiking Neural Networks for Efficient In-Memory Computing.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network Input-Aware Dynamic Timestep Spiking Neural Networks for Efficient In-Memory Computing

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-22T23:37:15.371141Z

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 d1cdeffd-f969-4dec-834e-fb20d715789d · outbound

This paper cites Deep spiking neural networks for large vocabulary automatic speech recognition.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network Deep spiking neural networks for large vocabulary automatic speech recognition

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.599282Z

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 8999a1e8-20d3-4e75-9ec9-17c3598e68e4 · outbound

This paper cites Spiking neural networks with improved inherent recurrence dynamics for sequential learning.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network Spiking neural networks with improved inherent recurrence dynamics for sequential learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.527924Z

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 5d753b21-be97-4c2a-8331-d23470876405 · outbound

This paper cites Towards Energy-Efficient, Low-Latency and Accurate Spiking LSTMs.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network Towards Energy-Efficient, Low-Latency and Accurate Spiking LSTMs

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-22T23:37:15.362782Z

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 3c2990ad-a52a-4659-b0ee-b1de5c586a78 · outbound

This paper cites Sparse compressed spiking neural network accelerator for object detection.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network Sparse compressed spiking neural network accelerator for object detection

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.523083Z

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 4749d92f-6013-476f-888e-fc356ffd92a6 · outbound

This paper cites SpinalFlow: an architecture and dataflow tailored for spiking neural networks.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network SpinalFlow: an architecture and dataflow tailored for spiking neural networks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.532660Z

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 aa5c50c8-9c3f-45bb-9a6a-ba4fd7fd989b · outbound

This paper cites A 24.3µJ/image SNN accelerator for DVS-gesture with WS-LOS dataflow and sparse methods.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network A 24.3µJ/image SNN accelerator for DVS-gesture with WS-LOS dataflow and sparse methods

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-22T23:37:15.331052Z

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 4c692264-2b4a-4e8c-9244-ed6072a3588a · outbound

This paper cites Training spiking neural networks using lessons from deep learning.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network Training spiking neural networks using lessons from deep learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.590096Z

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 b3bf0d06-fb4d-46c3-98b6-76f220baf0f7 · outbound

This paper cites DIET-SNN: a low-latency spiking neural network with direct input encoding and leakage and threshold optimization.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network DIET-SNN: a low-latency spiking neural network with direct input encoding and leakage and threshold optimization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.603525Z

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 fc2da858-8223-46b7-b530-9793b832bca0 · outbound

This paper cites Temporal efficient training of spiking neural network via gradient re-weighting.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network Temporal efficient training of spiking neural network via gradient re-weighting

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.619251Z

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 a562d349-8075-454b-ab17-0c79b08b058f · outbound

This paper cites Efficient processing of deep neural networks: a tutorial and survey.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network Efficient processing of deep neural networks: a tutorial and survey

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.559713Z

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 7cbb7d18-cc75-43dc-a1c2-62b7ec6443fb · outbound

This paper cites Rethinking the value of network pruning.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network Rethinking the value of network pruning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.630975Z

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 743a1b24-3ff5-4db5-a7a5-df5524f15478 · outbound

This paper cites Towards model compression for deep learning based speech enhancement.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network Towards model compression for deep learning based speech enhancement

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.607363Z

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 e61ec250-27ad-4cdc-9579-1a1e1cd044c5 · outbound

This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-05-22T23:37:15.346407Z

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 ee184338-60cb-4dc9-a179-a0f7c3431c44 · outbound

This paper cites Supporting compressed-sparse activations and weights on SIMD-like accelerator for sparse convolutional neural networks.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network Supporting compressed-sparse activations and weights on SIMD-like accelerator for sparse convolutional neural networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.564521Z

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 59f89ac0-f14f-4f1e-bb09-cd000c26aeb7 · outbound

This paper cites A novel zero weight/activation-aware hardware architecture of convolutional neural network.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network A novel zero weight/activation-aware hardware architecture of convolutional neural network

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.627118Z

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 c2d878c5-d561-4e88-a766-ba80c2483bda · outbound

This paper cites EIE: efficient inference engine on compressed deep neural network.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network EIE: efficient inference engine on compressed deep neural network

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.568852Z

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 4b2cb942-9d68-458e-89fd-46966261cde0 · outbound

This paper cites Cnvlutin: ineffectual-neuron-free deep neural net- work computing.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network Cnvlutin: ineffectual-neuron-free deep neural net- work computing

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.554642Z

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 ec6a16eb-6771-4dde-b3ba-5bfe91d2b51b · outbound

This paper cites DARPA TIMIT acoustic-phonetic continous speech corpus CD-ROM. NIST speech DISC 1-1.1.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network DARPA TIMIT acoustic-phonetic continous speech corpus CD-ROM. NIST speech DISC 1-1.1

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.615101Z

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 561cd63c-eaa9-49fd-a783-f5a912878f43 · outbound

This paper cites The PyTorch-Kaldi speech recognition toolkit.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network The PyTorch-Kaldi speech recognition toolkit

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.536905Z

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 5e3b79ae-ca0c-4ce1-a205-eac96e776dab · outbound

This paper cites His research interests include VLSI design, com- puter architecture, and platform-based SoC design methodologies.

A 71.2-$\mu$W Speech Recognition Accelerator with Recurrent Spiking Neural Network His research interests include VLSI design, com- puter architecture, and platform-based SoC design methodologies

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T23:37:15.594777Z

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.