http://iis-projects.ee.ethz.ch/index.php?title=A_Recurrent_Neural_Network_Speech_Recognition_Chip&feed=atom&action=history
A Recurrent Neural Network Speech Recognition Chip - Revision history
2024-03-29T06:22:58Z
Revision history for this page on the wiki
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http://iis-projects.ee.ethz.ch/index.php?title=A_Recurrent_Neural_Network_Speech_Recognition_Chip&diff=5800&oldid=prev
Paulsc at 11:38, 10 November 2020
2020-11-10T11:38:15Z
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 11:38, 10 November 2020</td>
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<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>===Status: <del class="diffchange diffchange-inline">In Progress</del>===</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>===Status: <ins class="diffchange diffchange-inline">Completed</ins>===</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Igor Susmelj, Gianna Paulin</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Igor Susmelj, Gianna Paulin</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>: Supervisor: [[:User:Fconti | Francesco Conti]]</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>: Supervisor: [[:User:Fconti | Francesco Conti]]</div></td></tr>
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Paulsc
http://iis-projects.ee.ethz.ch/index.php?title=A_Recurrent_Neural_Network_Speech_Recognition_Chip&diff=5546&oldid=prev
Paulin at 14:22, 30 October 2020
2020-10-30T14:22:34Z
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 14:22, 30 October 2020</td>
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Paulin
http://iis-projects.ee.ethz.ch/index.php?title=A_Recurrent_Neural_Network_Speech_Recognition_Chip&diff=2331&oldid=prev
Lukasc at 12:38, 5 September 2016
2016-09-05T12:38:45Z
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Lukasc
http://iis-projects.ee.ethz.ch/index.php?title=A_Recurrent_Neural_Network_Speech_Recognition_Chip&diff=2330&oldid=prev
Lukasc at 12:37, 5 September 2016
2016-09-05T12:37:44Z
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 12:37, 5 September 2016</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>One particularly attractive application for embedded and mobile devices is the extraction of phonemes (the basic components of speech) and of words out of raw audio coming from a microphone.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>One particularly attractive application for embedded and mobile devices is the extraction of phonemes (the basic components of speech) and of words out of raw audio coming from a microphone.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>For highly energy-constrained devices such as wearable devices, being able to extract such high-level information within a limited power envelope and in a fast time would be an extremely attractive feature, enabling for example smart wake-up and search of local information on a smartwatch without the need to communicate with an external, more powerful device "on the cloud". Therefore, a low-power, energy efficient ASIC able to implement a generic deep RNN would be extremely significant with respect to this purpose.</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>For highly energy-constrained devices such as wearable devices, being able to extract such high-level information within a limited power envelope and in a fast time would be an extremely attractive feature, enabling for example smart wake-up and search of local information on a smartwatch without the need to communicate with an external, more powerful device "on the cloud". Therefore, a low-power, energy<ins class="diffchange diffchange-inline">-</ins>efficient ASIC able to implement a generic deep RNN would be extremely significant with respect to this purpose.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>In this project, you will work on the development of such a chip. You will be provided with a high-level golden model in Python or Matlab and you will:</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>In this project, you will work on the development of such a chip. You will be provided with a high-level golden model in Python or Matlab and you will:</div></td></tr>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div># design a chip using the deep RNN core developed in the previous points.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div># design a chip using the deep RNN core developed in the previous points.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>===Status: <del class="diffchange diffchange-inline">Available </del>===</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>===Status: <ins class="diffchange diffchange-inline">In Progress</ins>===</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">: Looking for 2-3 Master students</del></div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">Igor Susmelj, Gianna Paulin</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>: Supervisor: [[:User:Fconti | Francesco Conti]]</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>: Supervisor: [[:User:Fconti | Francesco Conti]]</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>: Cosupervisor: [[:User:lukasc | Lukas Cavigelli]]</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>: Cosupervisor: [[:User:lukasc | Lukas Cavigelli]]</div></td></tr>
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Lukasc
http://iis-projects.ee.ethz.ch/index.php?title=A_Recurrent_Neural_Network_Speech_Recognition_Chip&diff=2323&oldid=prev
Lukasc at 09:36, 1 September 2016
2016-09-01T09:36:51Z
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 09:36, 1 September 2016</td>
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Lukasc
http://iis-projects.ee.ethz.ch/index.php?title=A_Recurrent_Neural_Network_Speech_Recognition_Chip&diff=2126&oldid=prev
Fconti: Created page with "600px ==Short Description== '''Deep Recurrent Neural Networks''' have recently received a lot of attention as they are able to process highly time-corr..."
2016-04-14T16:40:07Z
<p>Created page with "<a href="/index.php?title=File:Rnn.jpg" title="File:Rnn.jpg">thumb|600px</a> ==Short Description== '''Deep Recurrent Neural Networks''' have recently received a lot of attention as they are able to process highly time-corr..."</p>
<p><b>New page</b></p><div>[[File:rnn.jpg|thumb|600px]]<br />
==Short Description==<br />
<br />
'''Deep Recurrent Neural Networks''' have recently received a lot of attention as they are able to process highly time-correlated data such as written language, speech and even code (e.g. [http://karpathy.github.io/2015/05/21/rnn-effectiveness/]) and extract high-level semantic information.<br />
One particularly attractive application for embedded and mobile devices is the extraction of phonemes (the basic components of speech) and of words out of raw audio coming from a microphone.<br />
<br />
For highly energy-constrained devices such as wearable devices, being able to extract such high-level information within a limited power envelope and in a fast time would be an extremely attractive feature, enabling for example smart wake-up and search of local information on a smartwatch without the need to communicate with an external, more powerful device "on the cloud". Therefore, a low-power, energy efficient ASIC able to implement a generic deep RNN would be extremely significant with respect to this purpose.<br />
<br />
In this project, you will work on the development of such a chip. You will be provided with a high-level golden model in Python or Matlab and you will:<br />
# develop an application-specific architecture to implement a deep RNN, correctly implementing the functionality specified in the golden model;<br />
# use open-source hardware IPs from the PULP project [http://www.pulp-platform.org/] to implement peripheral functionality for the chip and, if necessary, a small internal microcontroller using the PULPino MCU;<br />
# design a chip using the deep RNN core developed in the previous points.<br />
<br />
===Status: Available ===<br />
: Looking for 2-3 Master students<br />
: Supervisor: [[:User:Fconti | Francesco Conti]]<br />
: Cosupervisor: [[:User:lukasc | Lukas Cavigelli]]<br />
<br />
===Prerequisites===<br />
: Familiarity with a scripting environment (Matlab and/or Python+Numpy).<br />
: Knowledge of a hardware design language such as (System)Verilog or VHDL.<br />
: VLSI1 and enrolment in VLSI2.<br />
: At least one student has to test the chip as part of the VLSI3 lecture, if the ASIC will be manufactured.<br />
<br />
===Character===<br />
: 10% Theory<br />
: 60% Architecture, HW Design & Verification<br />
: 30% VLSI Back-end Design<br />
<br />
===Professor===<br />
: [http://www.iis.ee.ethz.ch/portrait/staff/lbenini.en.html Luca Benini]<br />
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==Links and References== <br />
* [http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/ (1)] WildML, Recurrent Neural Networks Tutorial.<br />
* [http://arxiv.org/pdf/1303.5778v1.pdf (2)] A. Graves, A. Mohamed and G. Hinton, Speech Recognition with Deep Recurrent Neural Networks.<br />
* [http://www.jmlr.org/proceedings/papers/v32/graves14.pdf (3)] A. Graves and N. Jaitly, Towards End-to-End Speech Recognition with Recurrent Neural Networks.<br />
* [http://arxiv.org/pdf/1507.06947v1.pdf (4)] H. Sak, A. Senior, K. Rao, F. Beaufays, Fast and Accurate Recurrent Neural Network Acoustic Models for Speech Recognition.<br />
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Fconti