Zobrazeno 1 - 10
of 11
pro vyhledávání: '"En-Yu Yang"'
Autor:
Georgios Zacharopoulos, Adel Ejjeh, Ying Jing, En-Yu Yang, Tianyu Jia, Iulian Brumar, Jeremy Intan, Muhammad Huzaifa, Sarita Adve, Vikram Adve, Gu-Yeon Wei, David Brooks
Publikováno v:
ACM Transactions on Embedded Computing Systems. 22:1-23
The design of heterogeneous systems that include domain specific accelerators is a challenging and time-consuming process. While taking into account area constraints, designers must decide which parts of an application to accelerate in hardware and w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::10a2db66e4a9d7ce6d2b85e2ce84213d
https://doi.org/10.1145/3580394
https://doi.org/10.1145/3580394
Autor:
ZACHAROPOULOS, GEORGIOS, EJJEH, ADEL, YING JING, EN-YU YANG, TIANYU JIA, BRUMAR, IULIAN, INTAN, JEREMY, HUZAIFA, MUHAMMAD, ADVE, SARITA, ADVE, VIKRAM, GU-YEON WEI, BROOKS, DAVID
Publikováno v:
ACM Transactions on Embedded Computing Systems; May2023, Vol. 22 Issue 3, p1-23, 23p
Autor:
Tianyu Jia, En-Yu Yang, Yu-Shun Hsiao, Jonathan Cruz, David Brooks, Gu-Yeon Wei, Vijay Janapa Reddi
Autonomous machines (e.g., vehicles, mobile robots, drones) require sophisticated 3D mapping to perceive the dynamic environment. However, maintaining a real-time 3D map is expensive both in terms of compute and memory requirements, especially for re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::967c076967c104d08f1ca361b3713a47
Autor:
En-Yu Yang, David Brooks, Alexander M. Rush, Marco Donato, Coleman Hooper, Yuji Chai, Glenn G. Ko, Paul N. Whatmough, Gu-Yeon Wei, Thierry Tambe
Publikováno v:
HCS
In this work, we present SM6, an SoC architecture for real-time denoised speech and NLP pipelines, featuring (1) MSSE: an unsupervised probabilistic sound source separation accelerator, (2) FlexNLP: a programmable inference accelerator for attention-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::654237ef7679dcb15992c7daf7e7f34d
https://doi.org/10.1109/hcs52781.2021.9567180
https://doi.org/10.1109/hcs52781.2021.9567180
Publikováno v:
ASAP
Deep neural networks (DNN) have become ubiquitous and dominant in various application domains due to its state-of-the-art learning capabilities. To run compute and memory intensive DNN models, designing specialized hardware accelerators becomes the c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d05b149717148750590f5b3492983bc5
https://doi.org/10.1109/asap52443.2021.00046
https://doi.org/10.1109/asap52443.2021.00046
Autor:
Gu-Yeon Wei, Coleman Hooper, Glenn G. Ko, Thierry Tambe, Paul N. Whatmough, Alexander M. Rush, En-Yu Yang, Marco Donato, David Brooks, Yuji Chai
Publikováno v:
ISSCC
Automatic speech recognition (ASR) using deep learning is essential for user interfaces on IoT devices. However, previously published ASR chips [4 –7] do not consider realistic operating conditions, which are typically noisy and may include more th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fea2e13800304170161d2fbb93e1eac6
https://doi.org/10.1109/isscc42613.2021.9366062
https://doi.org/10.1109/isscc42613.2021.9366062
Autor:
Alexander M. Rush, Vijay Janapa Reddi, David Brooks, En-Yu Yang, Zishen Wan, Yuntian Deng, Thierry Tambe, Gu-Yeon Wei
Publikováno v:
DAC
Conventional hardware-friendly quantization methods, such as fixed-point or integer, tend to perform poorly at very low precision as their shrunken dynamic ranges cannot adequately capture the wide data distributions commonly seen in sequence transdu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a3d0e4eb52bc81e2b24cc86fa8f695c3
https://doi.org/10.1109/dac18072.2020.9218516
https://doi.org/10.1109/dac18072.2020.9218516
Autor:
Alexander M. Rush, Marco Donato, En-Yu Yang, Gu-Yeon Wei, Paul N. Whatmough, Thierry Tambe, Lillian Pentecost, Coleman Hooper, Victor Sanh, Tianyu Jia, David Brooks
Publikováno v:
MICRO
Transformer-based language models such as BERT provide significant accuracy improvement for a multitude of natural language processing (NLP) tasks. However, their hefty computational and memory demands make them challenging to deploy to resource-cons
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::34233c7677dd89f6e616eef7718a10d9
Autor:
Tambe, Thierry, En-Yu Yang, Zishen Wan, Yuntian Deng, Reddi, Vijay Janapa, Rush, Alexander, Brooks, David, Gu-Yeon Wei
Publikováno v:
DAC: Annual ACM/IEEE Design Automation Conference; 2020, Issue 57, p299-304, 6p
Autor:
Yun-Sheng Chang, Tzu-Hsiang Hsu, Kea-Tiong Tang, Ren-Shuo Liu, En-Yu Yang, Wei-Hao Chen, Yen-Kai Chen, K. C. Li, Cheng-Han Yang, K. C. Hsu, Meng-Fan Chang, Cheng-Xin Xue, Ya-Chin King, Pin-Yi Li, Wei-Yu Lin, Chih-Cheng Hsieh, Chorng-Jung Lin
Publikováno v:
ISSCC
Many artificial intelligence (AI) edge devices use nonvolatile memory (NVM) to store the weights for the neural network (trained off-line on an AI server), and require low-energy and fast I/O accesses. The deep neural networks (DNN) used by AI proces
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0756debd5121537a0935e2c31fe8c314
https://doi.org/10.1109/isscc.2018.8310400
https://doi.org/10.1109/isscc.2018.8310400