Search Results - "Schaal, Marcel"

Efficient AI System Design With Cross-Layer Approximate Computing by Venkataramani, Swagath, Sun, Xiao, Wang, Naigang, Chen, Chia-Yu, Choi, Jungwook, Kang, Mingu, Agarwal, Ankur, Oh, Jinwook, Jain, Shubham, Babinsky, Tina, Cao, Nianzheng, Fox, Thomas, Fleischer, Bruce, Gristede, George, Guillorn, Michael, Haynie, Howard, Inoue, Hiroshi, Ishizaki, Kazuaki, Klaiber, Michael, Lo, Shih-Hsien, Maier, Gary, Mueller, Silvia, Scheuermann, Michael, Ogawa, Eri, Schaal, Marcel, Serrano, Mauricio, Silberman, Joel, Vezyrtzis, Christos, Wang, Wei, Yee, Fanchieh, Zhang, Jintao, Ziegler, Matthew, Zhou, Ching, Ohara, Moriyoshi, Lu, Pong-Fei, Curran, Brian, Shukla, Sunil, Srinivasan, Vijayalakshmi, Chang, Leland, Gopalakrishnan, Kailash

“…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…”
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A 7-nm Four-Core Mixed-Precision AI Chip With 26.2-TFLOPS Hybrid-FP8 Training, 104.9-TOPS INT4 Inference, and Workload-Aware Throttling by Lee, Sae Kyu, Agrawal, Ankur, Silberman, Joel, Ziegler, Matthew, Kang, Mingu, Venkataramani, Swagath, Cao, Nianzheng, Fleischer, Bruce, Guillorn, Michael, Cohen, Matthew, Mueller, Silvia M., Oh, Jinwook, Lutz, Martin, Jung, Jinwook, Koswatta, Siyu, Zhou, Ching, Zalani, Vidhi, Kar, Monodeep, Bonanno, James, Casatuta, Robert, Chen, Chia-Yu, Choi, Jungwook, Haynie, Howard, Herbert, Alyssa, Jain, Radhika, Kim, Kyu-Hyoun, Li, Yulong, Ren, Zhibin, Rider, Scot, Schaal, Marcel, Schelm, Kerstin, Scheuermann, Michael R., Sun, Xiao, Tran, Hung, Wang, Naigang, Wang, Wei, Zhang, Xin, Shah, Vinay, Curran, Brian, Srinivasan, Vijayalakshmi, Lu, Pong-Fei, Shukla, Sunil, Gopalakrishnan, Kailash, Chang, Leland

“…Reduced precision computation is a key enabling factor for energy-efficient acceleration of deep learning (DL) applications. This article presents a 7-nm…”
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Power-Limited Inference Performance Optimization Using a Software-Assisted Peak Current Regulation Scheme in a 5-nm AI SoC by Kar, Monodeep, Silberman, Joel, Venkataramani, Swagath, Srinivasan, Viji, Fleischer, Bruce, Rubin, Joshua, Lancaster, JohnDavid, Lee, Saekyu, Cohen, Matthew, Ziegler, Matthew, Cao, Nianzheng, Woodward, Sandra, Agrawal, Ankur, Zhou, Ching, Chatarasi, Prasanth, Gooding, Thomas, Guillorn, Michael, Hekmatshoartabari, Bahman, Jacob, Philip, Jain, Radhika, Jain, Shubham, Jung, Jinwook, Kim, Kyu-Hyoun, Koswatta, Siyu, Lutz, Martin, Mannari, Alberto, Mathew, Abey K., Nair, Indira, Ranjan, Ashish, Ren, Zhibin, Rider, Scot, Rower, Thomas, Satterfield, David, Schaal, Marcel, Sen, Sanchari, Tellez, Gustavo, Tran, Hung, Wang, Wei, Zalani, Vidhi, Zhang, Jintao, Zhang, Xin, Shah, Vinay, Senger, Robert, Kumar, Arvind, Lu, Pong-Fei, Chang, Leland

“…Discrete AI inference cards, operating under form-factor and system-defined peak power constraints, must serve diverse inference requests with widely varying…”
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DeepTools: Compiler and Execution Runtime Extensions for RaPiD AI Accelerator by Venkataramani, Swagath, Choi, Jungwook, Srinivasan, Vijayalakshmi, Wang, Wei, Zhang, Jintao, Schaal, Marcel, Serrano, Mauricio J., Ishizaki, Kazuaki, Inoue, Hiroshi, Ogawa, Eri, Ohara, Moriyoshi, Chang, Leland, Gopalakrishnan, Kailash

“…The ubiquitous adoption of systems specialized for AI requires bridging two seemingly conflicting challenges—the need to deliver extreme processing…”
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RaPiD: AI Accelerator for Ultra-low Precision Training and Inference by Venkataramani, Swagath, Srinivasan, Vijayalakshmi, Wang, Wei, Sen, Sanchari, Zhang, Jintao, Agrawal, Ankur, Kar, Monodeep, Jain, Shubham, Mannari, Alberto, Tran, Hoang, Li, Yulong, Ogawa, Eri, Ishizaki, Kazuaki, Inoue, Hiroshi, Schaal, Marcel, Serrano, Mauricio, Choi, Jungwook, Sun, Xiao, Wang, Naigang, Chen, Chia-Yu, Allain, Allison, Bonano, James, Cao, Nianzheng, Casatuta, Robert, Cohen, Matthew, Fleischer, Bruce, Guillorn, Michael, Haynie, Howard, Jung, Jinwook, Kang, Mingu, Kim, Kyu-hyoun, Koswatta, Siyu, Lee, Saekyu, Lutz, Martin, Mueller, Silvia, Oh, Jinwook, Ranjan, Ashish, Ren, Zhibin, Rider, Scot, Schelm, Kerstin, Scheuermann, Michael, Silberman, Joel, Yang, Jie, Zalani, Vidhi, Zhang, Xin, Zhou, Ching, Ziegler, Matt, Shah, Vinay, Ohara, Moriyoshi, Lu, Pong-Fei, Curran, Brian, Shukla, Sunil, Chang, Leland, Gopalakrishnan, Kailash

“…The growing prevalence and computational demands of Artificial Intelligence (AI) workloads has led to widespread use of hardware accelerators in their…”
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Behavioral changes in response to sound exposure and no spatial avoidance of noisy conditions in captive zebrafish by Neo, Yik Yaw, Parie, Lisa, Bakker, Frederique, Snelderwaard, Peter, Tudorache, Christian, Schaaf, Marcel, Slabbekoorn, Hans

“…Auditory sensitivity in fish serves various important functions, but also makes fish susceptible to noise pollution. Human-generated sounds may affect…”
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14.1 A Software-Assisted Peak Current Regulation Scheme to Improve Power-Limited Inference Performance in a 5nm AI SoC by Kar, Monodeep, Silberman, Joel, Venkataramani, Swagath, Srinivasan, Viji, Fleischer, Bruce, Rubin, Joshua, Lancaster, JohnDavid, Lee, Saekyu, Cohen, Matthew, Ziegler, Matthew, Cao, Nianzheng, Woodward, Sandra, Agrawal, Ankur, Zhou, Ching, Chatarasi, Prasanth, Gooding, Thomas, Guillorn, Michael, Hekmatshoartabari, Bahman, Jacob, Philip, Jain, Radhika, Jain, Shubham, Jung, Jinwook, Kim, Kyu-Hyoun, Koswatta, Siyu, Lutz, Martin, Mannari, Alberto, Mathew, Abey, Nair, Indira, Ranjan, Ashish, Ren, Zhibin, Rider, Scot, Roewer, Thomas, Satterfield, David, Schaal, Marcel, Sen, Sanchari, Tellez, Gustavo, Tran, Hung, Wang, Wei, Zalani, Vidhi, Zhang, Jintao, Zhang, Xin, Shah, Vinay, Senger, Robert, Kumar, Arvind, Lu, Pong-Fei, Chang, Leland

“…The rapid emergence of AI models, specifically large language models (LLMs) requiring large amounts of compute, drives the need for dedicated AI inference…”
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Test Strategies for Reconfigurable Scan Networks by Kochte, Michael A., Baranowski, Rafal, Schaal, Marcel, Wunderlich, Hans-Joachim

“…On-chip infrastructure is an essential part of today's complex designs and enables their cost-efficient manufacturing and operation. The diversity and high…”
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9.1 A 7nm 4-Core AI Chip with 25.6TFLOPS Hybrid FP8 Training, 102.4TOPS INT4 Inference and Workload-Aware Throttling by Agrawal, Ankur, Lee, Sae Kyu, Silberman, Joel, Ziegler, Matthew, Kang, Mingu, Venkataramani, Swagath, Cao, Nianzheng, Fleischer, Bruce, Guillorn, Michael, Cohen, Matthew, Mueller, Silvia, Oh, Jinwook, Lutz, Martin, Jung, Jinwook, Koswatta, Siyu, Zhou, Ching, Zalani, Vidhi, Bonanno, James, Casatuta, Robert, Chen, Chia-Yu, Choi, Jungwook, Haynie, Howard, Herbert, Alyssa, Jain, Radhika, Kar, Monodeep, Kim, Kyu-Hyoun, Li, Yulong, Ren, Zhibin, Rider, Scot, Schaal, Marcel, Schelm, Kerstin, Scheuermann, Michael, Sun, Xiao, Tran, Hung, Wang, Naigang, Wang, Wei, Zhang, Xin, Shah, Vinay, Curran, Brian, Srinivasan, Vijayalakshmi, Lu, Pong-Fei, Shukla, Sunil, Chang, Leland, Gopalakrishnan, Kailash

“…Low-precision computation is the key enabling factor to achieve high compute densities (TOPS/W and TOPS/mm 2 ) in AI hardware accelerators across cloud and…”
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Efficient fault simulation on many-core processors by Kochte, Michael A, Schaal, Marcel, Wunderlich, Hans-Joachim, Zoellin, Christian G

“…Fault simulation is essential in test generation, design for test and reliability assessment of integrated circuits. Reliability analysis and the simulation of…”
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A 3.0 TFLOPS 0.62V Scalable Processor Core for High Compute Utilization AI Training and Inference by Oh, Jinwook, Lee, Sae Kyu, Kang, Mingu, Ziegler, Matthew, Silberman, Joel, Agrawal, Ankur, Venkataramani, Swagath, Fleischer, Bruce, Guillorn, Michael, Choi, Jungwook, Wang, Wei, Mueller, Silvia, Ben-Yehuda, Shimon, Bonanno, James, Cao, Nianzheng, Casatuta, Robert, Chen, Chia-Yu, Cohen, Matt, Erez, Ophir, Fox, Thomas, Gristede, George, Haynie, Howard, Ivanov, Vicktoria, Koswatta, Siyu, Lo, Shih-Hsien, Lutz, Martin, Maier, Gary, Mesh, Alex, Nustov, Yevgeny, Rider, Scot, Schaal, Marcel, Scheuermann, Michael, Sun, Xiao, Wang, Naigang, Yee, Fanchieh, Zhou, Ching, Shah, Vinay, Curran, Brian, Srinivasan, Vijayalakshmi, Lu, Pong-Fei, Shukla, Sunil, Gopalakrishnan, Kailash, Chang, Leland

“…A processor core is presented for AI training and inference products. Leading-edge compute efficiency is achieved for robust fp16 training via efficient…”
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