Search Results - "Zalani, Vidhi"

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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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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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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Rapid and Holistic Technology Evaluation for Exploratory DTCO in Beyond 7nm Technologies by Na, Myung-Hee, Chu, Albert, Lee, Yoo-Mi, Young, Albert, Zalani, Vidhi, Tran, Hung H.

“…New device architectures such as horizontal Nanosheets have been seriously considered as a replacement for FinFET. A comprehensive, and realistic assessment of…”
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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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