Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Matthew M. Ziegler"'
Autor:
Jinwook Oh, Alyssa Herbert, Marcel Schaal, Zhibin Ren, Ching Zhou, Siyu Koswatta, Naigang Wang, Matthew Cohen, Vidhi Zalani, Howard M. Haynie, Matthew M. Ziegler, Sae Kyu Lee, Brian W. Curran, Monodeep Kar, Martin Lutz, Xin Zhang, Robert Casatuta, Vijayalakshmi Srinivasan, Nianzheng Cao, Sunil Shukla, Pong-Fei Lu, Leland Chang, Michael A. Guillorn, Bruce M. Fleischer, Michael R. Scheuermann, Joel Abraham Silberman, Kerstin Schelm, Vinay Velji Shah, Chia-Yu Chen, Kailash Gopalakrishnan, Swagath Venkataramani, Hung Tran, Mingu Kang, Wei Wang, Jungwook Choi, Scot H. Rider, Jinwook Jung, James J. Bonanno, Radhika Jain, Li Yulong, Xiao Sun, Silvia Melitta Mueller, Kyu-hyoun Kim, Ankur Agrawal
Publikováno v:
IEEE Journal of Solid-State Circuits. 57:182-197
Reduced precision computation is a key enabling factor for energy-efficient acceleration of deep learning (DL) applications. This article presents a 7-nm four-core mixed-precision artificial intelligence (AI) chip that supports four compute precision
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2998556a11fb9c2dbdc4775ef363b4bb
https://doi.org/10.1109/jssc.2021.3120113
https://doi.org/10.1109/jssc.2021.3120113
Autor:
Michael J. Klaiber, George D. Gristede, Shih-Hsien Lo, Hiroshi Inoue, Leland Chang, Christos Vezyrtzis, Jungwook Choi, Gary W. Maier, Fanchieh Yee, Shubham Jain, Brian W. Curran, Jintao Zhang, Mingu Kang, Howard M. Haynie, Mauricio J. Serrano, Pong-Fei Lu, Silvia Melitta Mueller, Matthew M. Ziegler, Bruce M. Fleischer, Kazuaki Ishizaki, Kailash Gopalakrishnan, Michael R. Scheuermann, Ankur Agarwal, Xiao Sun, Sunil Shukla, Thomas W. Fox, Vijayalakshmi Srinivasan, Tina Babinsky, Swagath Venkataramani, Michael A. Guillorn, Ching Zhou, Nianzheng Cao, Eri Ogawa, Naigang Wang, Moriyoshi Ohara, Joel Abraham Silberman, Jinwook Oh, Marcel Schaal, Chia-Yu Chen, Wei Wang
Publikováno v:
Proceedings of the IEEE. 108:2232-2250
Advances in deep neural networks (DNNs) and the availability of massive real-world data have enabled superhuman levels of accuracy on many AI tasks and ushered the explosive growth of AI workloads across the spectrum of computing devices. However, th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a039fc675f0bb5ab8047808cf3959486
https://doi.org/10.1109/jproc.2020.3029453
https://doi.org/10.1109/jproc.2020.3029453
Publikováno v:
IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 9:435-438
In recent years, interest in machine learning, deep learning, and more broadly AI has taken the computing industry by storm. Advancements in AI compute acceleration, along with algorithmic innovations and availability of training data, are widely cre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f37cd6199d93232791f3a4159429204e
https://doi.org/10.1109/jetcas.2019.2938901
https://doi.org/10.1109/jetcas.2019.2938901
Autor:
Matthew M. Ziegler, Sunil Shukla, Gary W. Maier, Jinwook Oh, Kailash Gopalakrishnan, Christos Vezyrtzis, Thomas W. Fox, Michael J. Klaiber, Howard M. Haynie, Swagath Venkataramani, Leland Chang, Jungwook Choi, Nianzheng Cao, Pong-Fei Lu, Pierce Chuang, Michael A. Guillorn, Brian W. Curran, Dongsoo Lee, Fanchieh Yee, Ankur Agrawal, Ching Zhou, Silvia Melitta Mueller, Naigang Wang, George D. Gristede, Bruce M. Fleischer, Michael R. Scheuermann, Tina Babinsky, Vijayalakshmi Srinivasan, Chia-Yu Chen, Joel Abraham Silberman, Shih-Hsien Lo
Publikováno v:
IEEE Solid-State Circuits Letters. 1:217-220
This letter presents a multi-TOPS AI accelerator core for deep learning training and inference. With a programmable architecture and custom ISA, this engine achieves >90% sustained utilization across the range of neural network topologies by employin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9fce01a03750f2d886bc8908899c58be
https://doi.org/10.1109/lssc.2019.2902738
https://doi.org/10.1109/lssc.2019.2902738
Autor:
Xin Zhang, Vijayalakshmi Srinivasan, Wei Wang, Jungwook Choi, Siyu Koswatta, Mingu Kang, Li Yulong, Bruce M. Fleischer, Radhika Jain, Michael R. Scheuermann, Kerstin Schelm, Kailash Gopalakrishnan, Monodeep Kar, Zhibin Ren, Michael A. Guillorn, Swagath Venkataramani, Howard M. Haynie, Xiao Sun, Matthew M. Ziegler, Hung Tran, Sae Kyu Lee, Kyu-hyoun Kim, Joel Abraham Silberman, Martin Lutz, Silvia Melitta Mueller, Sunil Shukla, Pong-Fei Lu, Vidhi Zalani, Ching Zhou, Brian W. Curran, Vinay Velji Shah, Naigang Wang, Leland Chang, Robert Casatuta, Alyssa Herbert, Nianzheng Cao, Scot H. Rider, Marcel Schaal, Ankur Agrawal, Jinwook Oh, Jinwook Jung, James J. Bonanno, Matthew Cohen, Chia-Yu Chen
Publikováno v:
ISSCC
Low-precision computation is the key enabling factor to achieve high compute densities (T0PS/W and T0PS/mm2) in AI hardware accelerators across cloud and edge platforms. However, robust deep learning (DL) model accuracy equivalent to high-precision c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::531f331936a1c81912ee062cdc44a72e
https://doi.org/10.1109/isscc42613.2021.9365791
https://doi.org/10.1109/isscc42613.2021.9365791
Autor:
Gary W. Maier, Wei Wang, Siyu Koswatta, Vijayalakshmi Srinivasan, Howard M. Haynie, George D. Gristede, Bruce M. Fleischer, Michael R. Scheuermann, Matthew M. Ziegler, Sunil Shukla, Jinwook Oh, Vicktoria Ivanov, Kailash Gopalakrishnan, Martin Lutz, Ching Zhou, Xiao Sun, Silvia Melitta Mueller, Brian W. Curran, Pong-Fei Lu, Thomas W. Fox, Swagath Venkataramani, Nianzheng Cao, Ankur Agrawal, Robert Casatuta, Naigang Wang, Jungwook Choi, Vinay Velji Shah, Alex Mesh, Marcel Schaal, Scot H. Rider, Fanchieh Yee, Joel Abraham Silberman, James J. Bonanno, Michael A. Guillorn, Mingu Kang, Sae Kyu Lee, Shimon Ben-Yehuda, Erez Ophir, Chia-Yu Chen, Matthew Cohen, Yevgeny Nustov, Leland Chang, Shih-Hsien Lo
Publikováno v:
VLSI Circuits
A processor core is presented for AI training and inference products. Leading-edge compute efficiency is achieved for robust fp16 training via efficient heterogeneous 2-D systolic array-SIMD compute engines leveraging compact DLFloat16 FPUs. Architec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ce688e26b703dfb25f1b3e3e01c4d0c8
https://doi.org/10.1109/vlsicircuits18222.2020.9162917
https://doi.org/10.1109/vlsicircuits18222.2020.9162917
Publikováno v:
IEEE Journal of Solid-State Circuits. 52:634-644
This paper presents a novel resonating inductor-based supply boosting scheme for low-voltage static random-access memories and logic in deep 14-nm silicon on insulator (SOI) FinFET technologies. The technique combines capacitive (C) and inductive (L)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::56653edda27051a35af32b26118fa795
https://doi.org/10.1109/jssc.2016.2628772
https://doi.org/10.1109/jssc.2016.2628772
Publikováno v:
DAC
Logic synthesis and physical design (LSPD) tools automate complex design tasks previously performed by human designers. One time-consuming task that remains manual is configuring the LSPD flow parameters, which significantly impacts design results. T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::33ca193f0a28d121314e82983dc5e84d
https://doi.org/10.1145/3316781.3323919
https://doi.org/10.1145/3316781.3323919
Autor:
Matthew M. Ziegler, Rajiv V. Joshi
Publikováno v:
MIXDES
This paper reviews key developments and the continuation of low power techniques needed from the Moore to AI eras. SRAM with a wider range of operation, from extreme low to high voltages, is enabled using novel circuit techniques and demonstrated for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::84fd7f1883956ddb3b1a235e6f170caa
https://doi.org/10.23919/mixdes.2019.8787172
https://doi.org/10.23919/mixdes.2019.8787172
Autor:
Matthew M. Ziegler, Alper Buyuktosunoglu, Karthik Swaminathan, Rajiv V. Joshi, Schuyler Eldridge, Pradip Bose, Martin Cochet, Arun Paidimarri, Nandhini Chandramoorthy
Publikováno v:
HPCA
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fc84e0bbe755276d972c11a6d7e2b1df
https://doi.org/10.1109/hpca.2019.00034
https://doi.org/10.1109/hpca.2019.00034