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ICASSP 2019

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The 2019 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit website

END-TO-END FEEDBACK LOSS IN SPEECH CHAIN FRAMEWORK VIA STRAIGHT-THROUGH ESTIMATOR


The speech chain mechanism integrates automatic speech recognition (ASR) and text-to-speech synthesis (TTS) modules into a single cycle during training. In our previous work, we applied a speech chain mechanism as a semi-supervised learning. It provides the ability for ASR and TTS to assist each other when they receive unpaired data and let them infer the missing pair and optimize the model with reconstruction loss.

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Authors:
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura
Submitted On:
14 May 2019 - 8:26pm
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ICASSP19_Poster_V1.pdf

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[1] Andros Tjandra, Sakriani Sakti, Satoshi Nakamura, "END-TO-END FEEDBACK LOSS IN SPEECH CHAIN FRAMEWORK VIA STRAIGHT-THROUGH ESTIMATOR", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4519. Accessed: May. 30, 2020.
@article{4519-19,
url = {http://sigport.org/4519},
author = {Andros Tjandra; Sakriani Sakti; Satoshi Nakamura },
publisher = {IEEE SigPort},
title = {END-TO-END FEEDBACK LOSS IN SPEECH CHAIN FRAMEWORK VIA STRAIGHT-THROUGH ESTIMATOR},
year = {2019} }
TY - EJOUR
T1 - END-TO-END FEEDBACK LOSS IN SPEECH CHAIN FRAMEWORK VIA STRAIGHT-THROUGH ESTIMATOR
AU - Andros Tjandra; Sakriani Sakti; Satoshi Nakamura
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4519
ER -
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura. (2019). END-TO-END FEEDBACK LOSS IN SPEECH CHAIN FRAMEWORK VIA STRAIGHT-THROUGH ESTIMATOR. IEEE SigPort. http://sigport.org/4519
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura, 2019. END-TO-END FEEDBACK LOSS IN SPEECH CHAIN FRAMEWORK VIA STRAIGHT-THROUGH ESTIMATOR. Available at: http://sigport.org/4519.
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura. (2019). "END-TO-END FEEDBACK LOSS IN SPEECH CHAIN FRAMEWORK VIA STRAIGHT-THROUGH ESTIMATOR." Web.
1. Andros Tjandra, Sakriani Sakti, Satoshi Nakamura. END-TO-END FEEDBACK LOSS IN SPEECH CHAIN FRAMEWORK VIA STRAIGHT-THROUGH ESTIMATOR [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4519

BLHUC: BAYESIAN LEARNING OF HIDDEN UNIT CONTRIBUTIONS FOR DEEP NEURAL NETWORK SPEAKER ADAPTATION

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Authors:
Xurong Xie, Xunying Liu, Tan Lee, Shoukang Hu, Lan Wang
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14 May 2019 - 8:05pm
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BLHUC BAYESIAN LEARNING OF HIDDEN UNIT CONTRIBUTIONS FOR DEEP NEURAL NETWORK SPEAKER ADAPTATION.pdf

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[1] Xurong Xie, Xunying Liu, Tan Lee, Shoukang Hu, Lan Wang, "BLHUC: BAYESIAN LEARNING OF HIDDEN UNIT CONTRIBUTIONS FOR DEEP NEURAL NETWORK SPEAKER ADAPTATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4518. Accessed: May. 30, 2020.
@article{4518-19,
url = {http://sigport.org/4518},
author = {Xurong Xie; Xunying Liu; Tan Lee; Shoukang Hu; Lan Wang },
publisher = {IEEE SigPort},
title = {BLHUC: BAYESIAN LEARNING OF HIDDEN UNIT CONTRIBUTIONS FOR DEEP NEURAL NETWORK SPEAKER ADAPTATION},
year = {2019} }
TY - EJOUR
T1 - BLHUC: BAYESIAN LEARNING OF HIDDEN UNIT CONTRIBUTIONS FOR DEEP NEURAL NETWORK SPEAKER ADAPTATION
AU - Xurong Xie; Xunying Liu; Tan Lee; Shoukang Hu; Lan Wang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4518
ER -
Xurong Xie, Xunying Liu, Tan Lee, Shoukang Hu, Lan Wang. (2019). BLHUC: BAYESIAN LEARNING OF HIDDEN UNIT CONTRIBUTIONS FOR DEEP NEURAL NETWORK SPEAKER ADAPTATION. IEEE SigPort. http://sigport.org/4518
Xurong Xie, Xunying Liu, Tan Lee, Shoukang Hu, Lan Wang, 2019. BLHUC: BAYESIAN LEARNING OF HIDDEN UNIT CONTRIBUTIONS FOR DEEP NEURAL NETWORK SPEAKER ADAPTATION. Available at: http://sigport.org/4518.
Xurong Xie, Xunying Liu, Tan Lee, Shoukang Hu, Lan Wang. (2019). "BLHUC: BAYESIAN LEARNING OF HIDDEN UNIT CONTRIBUTIONS FOR DEEP NEURAL NETWORK SPEAKER ADAPTATION." Web.
1. Xurong Xie, Xunying Liu, Tan Lee, Shoukang Hu, Lan Wang. BLHUC: BAYESIAN LEARNING OF HIDDEN UNIT CONTRIBUTIONS FOR DEEP NEURAL NETWORK SPEAKER ADAPTATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4518

Fast MVAE: Joint separation and classification of mixed sources based on multichannel variational autoencoder with auxiliary classifier

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Authors:
Li Li, Hirokazu Kameoka, Shoji Makino
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14 May 2019 - 5:47pm
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Li2019ICASSP05poster_v2.pdf

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[1] Li Li, Hirokazu Kameoka, Shoji Makino, "Fast MVAE: Joint separation and classification of mixed sources based on multichannel variational autoencoder with auxiliary classifier", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4515. Accessed: May. 30, 2020.
@article{4515-19,
url = {http://sigport.org/4515},
author = {Li Li; Hirokazu Kameoka; Shoji Makino },
publisher = {IEEE SigPort},
title = {Fast MVAE: Joint separation and classification of mixed sources based on multichannel variational autoencoder with auxiliary classifier},
year = {2019} }
TY - EJOUR
T1 - Fast MVAE: Joint separation and classification of mixed sources based on multichannel variational autoencoder with auxiliary classifier
AU - Li Li; Hirokazu Kameoka; Shoji Makino
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4515
ER -
Li Li, Hirokazu Kameoka, Shoji Makino. (2019). Fast MVAE: Joint separation and classification of mixed sources based on multichannel variational autoencoder with auxiliary classifier. IEEE SigPort. http://sigport.org/4515
Li Li, Hirokazu Kameoka, Shoji Makino, 2019. Fast MVAE: Joint separation and classification of mixed sources based on multichannel variational autoencoder with auxiliary classifier. Available at: http://sigport.org/4515.
Li Li, Hirokazu Kameoka, Shoji Makino. (2019). "Fast MVAE: Joint separation and classification of mixed sources based on multichannel variational autoencoder with auxiliary classifier." Web.
1. Li Li, Hirokazu Kameoka, Shoji Makino. Fast MVAE: Joint separation and classification of mixed sources based on multichannel variational autoencoder with auxiliary classifier [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4515

Joint Separation and Dereverberation of Reverberant Mixture with Multichannel Variational Autoencoder

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Authors:
Hirokazu Kameoka, Li Li, Shogo Seki, Shoji Makino
Submitted On:
14 May 2019 - 5:42pm
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AASP_L4_2.pdf

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[1] Hirokazu Kameoka, Li Li, Shogo Seki, Shoji Makino, "Joint Separation and Dereverberation of Reverberant Mixture with Multichannel Variational Autoencoder", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4514. Accessed: May. 30, 2020.
@article{4514-19,
url = {http://sigport.org/4514},
author = {Hirokazu Kameoka; Li Li; Shogo Seki; Shoji Makino },
publisher = {IEEE SigPort},
title = {Joint Separation and Dereverberation of Reverberant Mixture with Multichannel Variational Autoencoder},
year = {2019} }
TY - EJOUR
T1 - Joint Separation and Dereverberation of Reverberant Mixture with Multichannel Variational Autoencoder
AU - Hirokazu Kameoka; Li Li; Shogo Seki; Shoji Makino
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4514
ER -
Hirokazu Kameoka, Li Li, Shogo Seki, Shoji Makino. (2019). Joint Separation and Dereverberation of Reverberant Mixture with Multichannel Variational Autoencoder. IEEE SigPort. http://sigport.org/4514
Hirokazu Kameoka, Li Li, Shogo Seki, Shoji Makino, 2019. Joint Separation and Dereverberation of Reverberant Mixture with Multichannel Variational Autoencoder. Available at: http://sigport.org/4514.
Hirokazu Kameoka, Li Li, Shogo Seki, Shoji Makino. (2019). "Joint Separation and Dereverberation of Reverberant Mixture with Multichannel Variational Autoencoder." Web.
1. Hirokazu Kameoka, Li Li, Shogo Seki, Shoji Makino. Joint Separation and Dereverberation of Reverberant Mixture with Multichannel Variational Autoencoder [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4514

Learning Discriminative Features in Sequence Training without Requiring Framewise Labelled Data

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Authors:
Jun Wang, Dan Su, Jie Chen, Shulin Feng, Dongpeng Ma, Na Li, Dong Yu
Submitted On:
15 May 2019 - 3:11am
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Slides.pdf

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[1] Jun Wang, Dan Su, Jie Chen, Shulin Feng, Dongpeng Ma, Na Li, Dong Yu, "Learning Discriminative Features in Sequence Training without Requiring Framewise Labelled Data", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4513. Accessed: May. 30, 2020.
@article{4513-19,
url = {http://sigport.org/4513},
author = {Jun Wang; Dan Su; Jie Chen; Shulin Feng; Dongpeng Ma; Na Li; Dong Yu },
publisher = {IEEE SigPort},
title = {Learning Discriminative Features in Sequence Training without Requiring Framewise Labelled Data},
year = {2019} }
TY - EJOUR
T1 - Learning Discriminative Features in Sequence Training without Requiring Framewise Labelled Data
AU - Jun Wang; Dan Su; Jie Chen; Shulin Feng; Dongpeng Ma; Na Li; Dong Yu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4513
ER -
Jun Wang, Dan Su, Jie Chen, Shulin Feng, Dongpeng Ma, Na Li, Dong Yu. (2019). Learning Discriminative Features in Sequence Training without Requiring Framewise Labelled Data. IEEE SigPort. http://sigport.org/4513
Jun Wang, Dan Su, Jie Chen, Shulin Feng, Dongpeng Ma, Na Li, Dong Yu, 2019. Learning Discriminative Features in Sequence Training without Requiring Framewise Labelled Data. Available at: http://sigport.org/4513.
Jun Wang, Dan Su, Jie Chen, Shulin Feng, Dongpeng Ma, Na Li, Dong Yu. (2019). "Learning Discriminative Features in Sequence Training without Requiring Framewise Labelled Data." Web.
1. Jun Wang, Dan Su, Jie Chen, Shulin Feng, Dongpeng Ma, Na Li, Dong Yu. Learning Discriminative Features in Sequence Training without Requiring Framewise Labelled Data [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4513

INDUCTIVE CONFORMAL PREDICTOR FOR SPARSE CODING CLASSIFIERS: APPLICATIONS TO IMAGE CLASSIFICATION


Conformal prediction uses the degree of strangeness (nonconformity) of new data instances to determine the confidence values of new predictions. We propose an inductive conformal predictor for sparse coding classifiers, referred to as ICP-SCC. Our contribution is twofold: first, we present two nonconformitymeasures that produce reliable confidence values; second, we propose a batchmode active learning algorithm within the conformal prediction framework to improve classification performance by selecting training instances based on two criteria, informativeness and diversity.

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Authors:
Kenneth E. Barner
Submitted On:
14 May 2019 - 10:41am
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Poster ICASSP 2019

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[1] Kenneth E. Barner, "INDUCTIVE CONFORMAL PREDICTOR FOR SPARSE CODING CLASSIFIERS: APPLICATIONS TO IMAGE CLASSIFICATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4512. Accessed: May. 30, 2020.
@article{4512-19,
url = {http://sigport.org/4512},
author = {Kenneth E. Barner },
publisher = {IEEE SigPort},
title = {INDUCTIVE CONFORMAL PREDICTOR FOR SPARSE CODING CLASSIFIERS: APPLICATIONS TO IMAGE CLASSIFICATION},
year = {2019} }
TY - EJOUR
T1 - INDUCTIVE CONFORMAL PREDICTOR FOR SPARSE CODING CLASSIFIERS: APPLICATIONS TO IMAGE CLASSIFICATION
AU - Kenneth E. Barner
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4512
ER -
Kenneth E. Barner. (2019). INDUCTIVE CONFORMAL PREDICTOR FOR SPARSE CODING CLASSIFIERS: APPLICATIONS TO IMAGE CLASSIFICATION. IEEE SigPort. http://sigport.org/4512
Kenneth E. Barner, 2019. INDUCTIVE CONFORMAL PREDICTOR FOR SPARSE CODING CLASSIFIERS: APPLICATIONS TO IMAGE CLASSIFICATION. Available at: http://sigport.org/4512.
Kenneth E. Barner. (2019). "INDUCTIVE CONFORMAL PREDICTOR FOR SPARSE CODING CLASSIFIERS: APPLICATIONS TO IMAGE CLASSIFICATION." Web.
1. Kenneth E. Barner. INDUCTIVE CONFORMAL PREDICTOR FOR SPARSE CODING CLASSIFIERS: APPLICATIONS TO IMAGE CLASSIFICATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4512

Phoneme Level Language Models for Sequence Based Low Resource ASR


Building multilingual and crosslingual models help bring different languages together in a language universal space. It allows models to share parameters and transfer knowledge across languages, enabling faster and better adaptation to a new language. These approaches are particularly useful for low resource languages. In this paper, we propose a phoneme-level language model that can be used multilingually and for crosslingual adaptation to a target language.

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Authors:
Siddharth Dalmia, Xinjian Li, Alan W Black, Florian Metze
Submitted On:
14 May 2019 - 10:39am
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PLMs_ICASSP_Poster (1).pdf

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[1] Siddharth Dalmia, Xinjian Li, Alan W Black, Florian Metze, "Phoneme Level Language Models for Sequence Based Low Resource ASR", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4511. Accessed: May. 30, 2020.
@article{4511-19,
url = {http://sigport.org/4511},
author = {Siddharth Dalmia; Xinjian Li; Alan W Black; Florian Metze },
publisher = {IEEE SigPort},
title = {Phoneme Level Language Models for Sequence Based Low Resource ASR},
year = {2019} }
TY - EJOUR
T1 - Phoneme Level Language Models for Sequence Based Low Resource ASR
AU - Siddharth Dalmia; Xinjian Li; Alan W Black; Florian Metze
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4511
ER -
Siddharth Dalmia, Xinjian Li, Alan W Black, Florian Metze. (2019). Phoneme Level Language Models for Sequence Based Low Resource ASR. IEEE SigPort. http://sigport.org/4511
Siddharth Dalmia, Xinjian Li, Alan W Black, Florian Metze, 2019. Phoneme Level Language Models for Sequence Based Low Resource ASR. Available at: http://sigport.org/4511.
Siddharth Dalmia, Xinjian Li, Alan W Black, Florian Metze. (2019). "Phoneme Level Language Models for Sequence Based Low Resource ASR." Web.
1. Siddharth Dalmia, Xinjian Li, Alan W Black, Florian Metze. Phoneme Level Language Models for Sequence Based Low Resource ASR [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4511

One-Bit Unlimited Sampling


Conventional analog–to–digital converters (ADCs) are limited in dynamic range. If a signal exceeds some prefixed threshold, the ADC saturates and the resulting signal is clipped, thus becoming prone to aliasing artifacts. Recent developments in ADC design allow to overcome this limitation: using modulo operation, the so called self-reset ADCs fold amplitudes which exceed the dynamic range. A new (unlimited) sampling theory is currently being developed in the context of this novel class of ADCs.

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Authors:
Felix Krahmer
Submitted On:
14 May 2019 - 10:46am
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ICASSP19_GBK.pdf

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[1] Felix Krahmer, "One-Bit Unlimited Sampling", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4510. Accessed: May. 30, 2020.
@article{4510-19,
url = {http://sigport.org/4510},
author = {Felix Krahmer },
publisher = {IEEE SigPort},
title = {One-Bit Unlimited Sampling},
year = {2019} }
TY - EJOUR
T1 - One-Bit Unlimited Sampling
AU - Felix Krahmer
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4510
ER -
Felix Krahmer. (2019). One-Bit Unlimited Sampling. IEEE SigPort. http://sigport.org/4510
Felix Krahmer, 2019. One-Bit Unlimited Sampling. Available at: http://sigport.org/4510.
Felix Krahmer. (2019). "One-Bit Unlimited Sampling." Web.
1. Felix Krahmer. One-Bit Unlimited Sampling [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4510

Subword Regularization and Beam Search Decoding for End-to-End Automatic Speech Recognition


In this paper, we experiment with the recently introduced subword regularization technique \cite{kudo2018subword} in the context of end-to-end automatic speech recognition (ASR). We present results from both attention-based and CTC-based ASR systems on two common benchmark datasets, the 80 hour Wall Street Journal corpus and 1,000 hour Librispeech corpus. We also introduce a novel subword beam search decoding algorithm that significantly improves the final performance of the CTC-based systems.

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Authors:
Jennifer Drexler, James Glass
Submitted On:
14 May 2019 - 9:04am
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ICASSP_poster_final.pdf

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[1] Jennifer Drexler, James Glass, "Subword Regularization and Beam Search Decoding for End-to-End Automatic Speech Recognition", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4509. Accessed: May. 30, 2020.
@article{4509-19,
url = {http://sigport.org/4509},
author = {Jennifer Drexler; James Glass },
publisher = {IEEE SigPort},
title = {Subword Regularization and Beam Search Decoding for End-to-End Automatic Speech Recognition},
year = {2019} }
TY - EJOUR
T1 - Subword Regularization and Beam Search Decoding for End-to-End Automatic Speech Recognition
AU - Jennifer Drexler; James Glass
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4509
ER -
Jennifer Drexler, James Glass. (2019). Subword Regularization and Beam Search Decoding for End-to-End Automatic Speech Recognition. IEEE SigPort. http://sigport.org/4509
Jennifer Drexler, James Glass, 2019. Subword Regularization and Beam Search Decoding for End-to-End Automatic Speech Recognition. Available at: http://sigport.org/4509.
Jennifer Drexler, James Glass. (2019). "Subword Regularization and Beam Search Decoding for End-to-End Automatic Speech Recognition." Web.
1. Jennifer Drexler, James Glass. Subword Regularization and Beam Search Decoding for End-to-End Automatic Speech Recognition [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4509

AN HETEROGENEOUS COMPILER OF DATAFLOW PROGRAMS FOR ZYNQ PLATFORMS


In recent years, the number and variety of heterogeneous multiprocessor system-on-chip MPSoCs, such as for instance Zynq platforms, has sensibly increased. However, today all design flow solutions capable of programming the different components of such platforms require to the designer either to modify the software or hardware based designs to obtain higher performance implementations. Thus, the developer needs to either rewrite functional blocks in HDL or to use high-level synthesis of C-like sequential languages with platform locked extensions.

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Authors:
Simone Casale Brunet, Romuald Mosqueron, Marco Mattavelli
Submitted On:
14 May 2019 - 8:07am
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main-poster.pdf

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[1] Simone Casale Brunet, Romuald Mosqueron, Marco Mattavelli, "AN HETEROGENEOUS COMPILER OF DATAFLOW PROGRAMS FOR ZYNQ PLATFORMS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4508. Accessed: May. 30, 2020.
@article{4508-19,
url = {http://sigport.org/4508},
author = {Simone Casale Brunet; Romuald Mosqueron; Marco Mattavelli },
publisher = {IEEE SigPort},
title = {AN HETEROGENEOUS COMPILER OF DATAFLOW PROGRAMS FOR ZYNQ PLATFORMS},
year = {2019} }
TY - EJOUR
T1 - AN HETEROGENEOUS COMPILER OF DATAFLOW PROGRAMS FOR ZYNQ PLATFORMS
AU - Simone Casale Brunet; Romuald Mosqueron; Marco Mattavelli
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4508
ER -
Simone Casale Brunet, Romuald Mosqueron, Marco Mattavelli. (2019). AN HETEROGENEOUS COMPILER OF DATAFLOW PROGRAMS FOR ZYNQ PLATFORMS. IEEE SigPort. http://sigport.org/4508
Simone Casale Brunet, Romuald Mosqueron, Marco Mattavelli, 2019. AN HETEROGENEOUS COMPILER OF DATAFLOW PROGRAMS FOR ZYNQ PLATFORMS. Available at: http://sigport.org/4508.
Simone Casale Brunet, Romuald Mosqueron, Marco Mattavelli. (2019). "AN HETEROGENEOUS COMPILER OF DATAFLOW PROGRAMS FOR ZYNQ PLATFORMS." Web.
1. Simone Casale Brunet, Romuald Mosqueron, Marco Mattavelli. AN HETEROGENEOUS COMPILER OF DATAFLOW PROGRAMS FOR ZYNQ PLATFORMS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4508

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