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  1. AU="Hechtman, Blake"
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  1. Book ; Online: TPU-KNN

    Chern, Felix / Hechtman, Blake / Davis, Andy / Guo, Ruiqi / Majnemer, David / Kumar, Sanjiv

    K Nearest Neighbor Search at Peak FLOP/s

    2022  

    Abstract: This paper presents a novel nearest neighbor search algorithm achieving TPU (Google Tensor Processing Unit) peak performance, outperforming state-of-the-art GPU algorithms with similar level of recall. The design of the proposed algorithm is motivated by ...

    Abstract This paper presents a novel nearest neighbor search algorithm achieving TPU (Google Tensor Processing Unit) peak performance, outperforming state-of-the-art GPU algorithms with similar level of recall. The design of the proposed algorithm is motivated by an accurate accelerator performance model that takes into account both the memory and instruction bottlenecks. Our algorithm comes with an analytical guarantee of recall in expectation and does not require maintaining sophisticated index data structure or tuning, making it suitable for applications with frequent updates. Our work is available in the open-source package of Jax and Tensorflow on TPU.
    Keywords Computer Science - Performance ; Computer Science - Machine Learning
    Publishing date 2022-06-28
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Book ; Online: Scaling Local Self-Attention for Parameter Efficient Visual Backbones

    Vaswani, Ashish / Ramachandran, Prajit / Srinivas, Aravind / Parmar, Niki / Hechtman, Blake / Shlens, Jonathon

    2021  

    Abstract: Self-attention has the promise of improving computer vision systems due to parameter-independent scaling of receptive fields and content-dependent interactions, in contrast to parameter-dependent scaling and content-independent interactions of ... ...

    Abstract Self-attention has the promise of improving computer vision systems due to parameter-independent scaling of receptive fields and content-dependent interactions, in contrast to parameter-dependent scaling and content-independent interactions of convolutions. Self-attention models have recently been shown to have encouraging improvements on accuracy-parameter trade-offs compared to baseline convolutional models such as ResNet-50. In this work, we aim to develop self-attention models that can outperform not just the canonical baseline models, but even the high-performing convolutional models. We propose two extensions to self-attention that, in conjunction with a more efficient implementation of self-attention, improve the speed, memory usage, and accuracy of these models. We leverage these improvements to develop a new self-attention model family, HaloNets, which reach state-of-the-art accuracies on the parameter-limited setting of the ImageNet classification benchmark. In preliminary transfer learning experiments, we find that HaloNet models outperform much larger models and have better inference performance. On harder tasks such as object detection and instance segmentation, our simple local self-attention and convolutional hybrids show improvements over very strong baselines. These results mark another step in demonstrating the efficacy of self-attention models on settings traditionally dominated by convolutional models.

    Comment: CVPR 2021 Oral
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 006
    Publishing date 2021-03-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: Automatic Cross-Replica Sharding of Weight Update in Data-Parallel Training

    Xu, Yuanzhong / Lee, HyoukJoong / Chen, Dehao / Choi, Hongjun / Hechtman, Blake / Wang, Shibo

    2020  

    Abstract: In data-parallel synchronous training of deep neural networks, different devices (replicas) run the same program with different partitions of the training batch, but weight update computation is repeated on all replicas, because the weights do not have a ...

    Abstract In data-parallel synchronous training of deep neural networks, different devices (replicas) run the same program with different partitions of the training batch, but weight update computation is repeated on all replicas, because the weights do not have a batch dimension to partition. This can be a bottleneck for performance and scalability in typical language models with large weights, and models with small per-replica batch size which is typical in large-scale training. This paper presents an approach to automatically shard the weight update computation across replicas with efficient communication primitives and data formatting, using static analysis and transformations on the training computation graph. We show this technique achieves substantial speedups on typical image and language models on Cloud TPUs, requiring no change to model code. This technique helps close the gap between traditionally expensive (ADAM) and cheap (SGD) optimizers, as they will only take a small part of training step time and have similar peak memory usage. It helped us to achieve state-of-the-art training performance in Google's MLPerf 0.6 submission.

    Comment: 12 pages, 23 figures, 1 table
    Keywords Computer Science - Distributed ; Parallel ; and Cluster Computing ; Computer Science - Machine Learning ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2020-04-28
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Large-Scale Discrete Fourier Transform on TPUs

    Lu, Tianjian / Chen, Yi-Fan / Hechtman, Blake / Wang, Tao / Anderson, John

    2020  

    Abstract: In this work, we present two parallel algorithms for the large-scale discrete Fourier transform (DFT) on Tensor Processing Unit (TPU) clusters. The two parallel algorithms are associated with two formulations of DFT: one is based on the Kronecker product, ...

    Abstract In this work, we present two parallel algorithms for the large-scale discrete Fourier transform (DFT) on Tensor Processing Unit (TPU) clusters. The two parallel algorithms are associated with two formulations of DFT: one is based on the Kronecker product, to be specific, dense matrix multiplications between the input data and the Vandermonde matrix, denoted as KDFT in this work; the other is based on the famous Cooley-Tukey algorithm and phase adjustment, denoted as FFT in this work. Both KDFT and FFT formulations take full advantage of TPU's strength in matrix multiplications. The KDFT formulation allows direct use of nonuniform inputs without additional step. In the two parallel algorithms, the same strategy of data decomposition is applied to the input data. Through the data decomposition, the dense matrix multiplications in KDFT and FFT are kept local within TPU cores, which can be performed completely in parallel. The communication among TPU cores is achieved through the one-shuffle scheme in both parallel algorithms, with which sending and receiving data takes place simultaneously between two neighboring cores and along the same direction on the interconnect network. The one-shuffle scheme is designed for the interconnect topology of TPU clusters, minimizing the time required by the communication among TPU cores. Both KDFT and FFT are implemented in TensorFlow. The three-dimensional complex DFT is performed on an example of dimension $8192 \times 8192 \times 8192$ with a full TPU Pod: the run time of KDFT is 12.66 seconds and that of FFT is 8.3 seconds. Scaling analysis is provided to demonstrate the high parallel efficiency of the two DFT implementations on TPUs.
    Keywords Computer Science - Mathematical Software ; Computer Science - Distributed ; Parallel ; and Cluster Computing
    Subject code 518
    Publishing date 2020-02-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: GSPMD

    Xu, Yuanzhong / Lee, HyoukJoong / Chen, Dehao / Hechtman, Blake / Huang, Yanping / Joshi, Rahul / Krikun, Maxim / Lepikhin, Dmitry / Ly, Andy / Maggioni, Marcello / Pang, Ruoming / Shazeer, Noam / Wang, Shibo / Wang, Tao / Wu, Yonghui / Chen, Zhifeng

    General and Scalable Parallelization for ML Computation Graphs

    2021  

    Abstract: We present GSPMD, an automatic, compiler-based parallelization system for common machine learning computation graphs. It allows users to write programs in the same way as for a single device, then give hints through a few annotations on how to distribute ...

    Abstract We present GSPMD, an automatic, compiler-based parallelization system for common machine learning computation graphs. It allows users to write programs in the same way as for a single device, then give hints through a few annotations on how to distribute tensors, based on which GSPMD will parallelize the computation. Its representation of partitioning is simple yet general, allowing it to express different or mixed paradigms of parallelism on a wide variety of models. GSPMD infers the partitioning for every operator in the graph based on limited user annotations, making it convenient to scale up existing single-device programs. It solves several technical challenges for production usage, such as static shape constraints, uneven partitioning, exchange of halo data, and nested operator partitioning. These techniques allow GSPMD to achieve 50% to 62% compute utilization on 128 to 2048 Cloud TPUv3 cores for models with up to one trillion parameters. GSPMD produces a single program for all devices, which adjusts its behavior based on a run-time partition ID, and uses collective operators for cross-device communication. This property allows the system itself to be scalable: the compilation time stays constant with increasing number of devices.
    Keywords Computer Science - Distributed ; Parallel ; and Cluster Computing ; Computer Science - Machine Learning
    Subject code 005 ; 006
    Publishing date 2021-05-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Scale MLPerf-0.6 models on Google TPU-v3 Pods

    Kumar, Sameer / Bitorff, Victor / Chen, Dehao / Chou, Chiachen / Hechtman, Blake / Lee, HyoukJoong / Kumar, Naveen / Mattson, Peter / Wang, Shibo / Wang, Tao / Xu, Yuanzhong / Zhou, Zongwei

    2019  

    Abstract: The recent submission of Google TPU-v3 Pods to the industry wide MLPerf v0.6 training benchmark demonstrates the scalability of a suite of industry relevant ML models. MLPerf defines a suite of models, datasets and rules to follow when benchmarking to ... ...

    Abstract The recent submission of Google TPU-v3 Pods to the industry wide MLPerf v0.6 training benchmark demonstrates the scalability of a suite of industry relevant ML models. MLPerf defines a suite of models, datasets and rules to follow when benchmarking to ensure results are comparable across hardware, frameworks and companies. Using this suite of models, we discuss the optimizations and techniques including choice of optimizer, spatial partitioning and weight update sharding necessary to scale to 1024 TPU chips. Furthermore, we identify properties of models that make scaling them challenging, such as limited data parallelism and unscaled weights. These optimizations contribute to record performance in transformer, Resnet-50 and SSD in the Google MLPerf-0.6 submission.
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Performance
    Publishing date 2019-09-20
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Exploring the limits of Concurrency in ML Training on Google TPUs

    Kumar, Sameer / Bradbury, James / Young, Cliff / Wang, Yu Emma / Levskaya, Anselm / Hechtman, Blake / Chen, Dehao / Lee, HyoukJoong / Deveci, Mehmet / Kumar, Naveen / Kanwar, Pankaj / Wang, Shibo / Wanderman-Milne, Skye / Lacy, Steve / Wang, Tao / Oguntebi, Tayo / Zu, Yazhou / Xu, Yuanzhong / Swing, Andy

    2020  

    Abstract: Recent results in language understanding using neural networks have required training hardware of unprecedentedscale, with thousands of chips cooperating on a single training run. This paper presents techniques to scaleML models on the Google TPU ... ...

    Abstract Recent results in language understanding using neural networks have required training hardware of unprecedentedscale, with thousands of chips cooperating on a single training run. This paper presents techniques to scaleML models on the Google TPU Multipod, a mesh with 4096 TPU-v3 chips. We discuss model parallelism toovercome scaling limitations from the fixed batch size in data parallelism, communication/collective optimizations,distributed evaluation of training metrics, and host input processing scaling optimizations. These techniques aredemonstrated in both the TensorFlow and JAX programming frameworks. We also present performance resultsfrom the recent Google submission to the MLPerf-v0.7 benchmark contest, achieving record training times from16 to 28 seconds in four MLPerf models on the Google TPU-v3 Multipod machine.
    Keywords Computer Science - Machine Learning ; Computer Science - Distributed ; Parallel ; and Cluster Computing
    Subject code 006
    Publishing date 2020-11-06
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Gemini

    Gemini Team / Anil, Rohan / Borgeaud, Sebastian / Wu, Yonghui / Alayrac, Jean-Baptiste / Yu, Jiahui / Soricut, Radu / Schalkwyk, Johan / Dai, Andrew M. / Hauth, Anja / Millican, Katie / Silver, David / Petrov, Slav / Johnson, Melvin / Antonoglou, Ioannis / Schrittwieser, Julian / Glaese, Amelia / Chen, Jilin / Pitler, Emily /
    Lillicrap, Timothy / Lazaridou, Angeliki / Firat, Orhan / Molloy, James / Isard, Michael / Barham, Paul R. / Hennigan, Tom / Lee, Benjamin / Viola, Fabio / Reynolds, Malcolm / Xu, Yuanzhong / Doherty, Ryan / Collins, Eli / Meyer, Clemens / Rutherford, Eliza / Moreira, Erica / Ayoub, Kareem / Goel, Megha / Tucker, George / Piqueras, Enrique / Krikun, Maxim / Barr, Iain / Savinov, Nikolay / Danihelka, Ivo / Roelofs, Becca / White, Anaïs / Andreassen, Anders / von Glehn, Tamara / Yagati, Lakshman / Kazemi, Mehran / Gonzalez, Lucas / Khalman, Misha / Sygnowski, Jakub / Frechette, Alexandre / Smith, Charlotte / Culp, Laura / Proleev, Lev / Luan, Yi / Chen, Xi / Lottes, James / Schucher, Nathan / Lebron, Federico / Rrustemi, Alban / Clay, Natalie / Crone, Phil / Kocisky, Tomas / Zhao, Jeffrey / Perz, Bartek / Yu, Dian / Howard, Heidi / Bloniarz, Adam / Rae, Jack W. / Lu, Han / Sifre, Laurent / Maggioni, Marcello / Alcober, Fred / Garrette, Dan / Barnes, Megan / Thakoor, Shantanu / Austin, Jacob / Barth-Maron, Gabriel / Wong, William / Joshi, Rishabh / Chaabouni, Rahma / Fatiha, Deeni / Ahuja, Arun / Liu, Ruibo / Li, Yunxuan / Cogan, Sarah / Chen, Jeremy / Jia, Chao / Gu, Chenjie / Zhang, Qiao / Grimstad, Jordan / Hartman, Ale Jakse / Chadwick, Martin / Tomar, Gaurav Singh / Garcia, Xavier / Senter, Evan / Taropa, Emanuel / Pillai, Thanumalayan Sankaranarayana / Devlin, Jacob / Laskin, Michael / Casas, Diego de Las / Valter, Dasha / Tao, Connie / Blanco, Lorenzo / Badia, Adrià Puigdomènech / Reitter, David / Chen, Mianna / Brennan, Jenny / Rivera, Clara / Brin, Sergey / Iqbal, Shariq / Surita, Gabriela / Labanowski, Jane / Rao, Abhi / Winkler, Stephanie / Parisotto, Emilio / Gu, Yiming / Olszewska, Kate / Zhang, Yujing / Addanki, Ravi / Miech, Antoine / Louis, Annie / Shafey, Laurent El / Teplyashin, Denis / Brown, Geoff / Catt, Elliot / Attaluri, Nithya / Balaguer, Jan / Xiang, Jackie / Wang, Pidong / Ashwood, Zoe / Briukhov, Anton / Webson, Albert / Ganapathy, Sanjay / Sanghavi, Smit / Kannan, Ajay / Chang, Ming-Wei / Stjerngren, Axel / Djolonga, Josip / Sun, Yuting / Bapna, Ankur / Aitchison, Matthew / Pejman, Pedram / Michalewski, Henryk / Yu, Tianhe / Wang, Cindy / Love, Juliette / Ahn, Junwhan / Bloxwich, Dawn / Han, Kehang / Humphreys, Peter / Sellam, Thibault / Bradbury, James / Godbole, Varun / Samangooei, Sina / Damoc, Bogdan / Kaskasoli, Alex / Arnold, Sébastien M. R. / Vasudevan, Vijay / Agrawal, Shubham / Riesa, Jason / Lepikhin, Dmitry / Tanburn, Richard / Srinivasan, Srivatsan / Lim, Hyeontaek / Hodkinson, Sarah / Shyam, Pranav / Ferret, Johan / Hand, Steven / Garg, Ankush / Paine, Tom Le / Li, Jian / Li, Yujia / Giang, Minh / Neitz, Alexander / Abbas, Zaheer / York, Sarah / Reid, Machel / Cole, Elizabeth / Chowdhery, Aakanksha / Das, Dipanjan / Rogozińska, Dominika / Nikolaev, Vitaly / Sprechmann, Pablo / Nado, Zachary / Zilka, Lukas / Prost, Flavien / He, Luheng / Monteiro, Marianne / Mishra, Gaurav / Welty, Chris / Newlan, Josh / Jia, Dawei / Allamanis, Miltiadis / Hu, Clara Huiyi / de Liedekerke, Raoul / Gilmer, Justin / Saroufim, Carl / Rijhwani, Shruti / Hou, Shaobo / Shrivastava, Disha / Baddepudi, Anirudh / Goldin, Alex / Ozturel, Adnan / Cassirer, Albin / Xu, Yunhan / Sohn, Daniel / Sachan, Devendra / Amplayo, Reinald Kim / Swanson, Craig / Petrova, Dessie / Narayan, Shashi / Guez, Arthur / Brahma, Siddhartha / Landon, Jessica / Patel, Miteyan / Zhao, Ruizhe / Villela, Kevin / Wang, Luyu / Jia, Wenhao / Rahtz, Matthew / Giménez, Mai / Yeung, Legg / Lin, Hanzhao / Keeling, James / Georgiev, Petko / Mincu, Diana / Wu, Boxi / Haykal, Salem / Saputro, Rachel / Vodrahalli, Kiran / Qin, James / Cankara, Zeynep / Sharma, Abhanshu / Fernando, Nick / Hawkins, Will / Neyshabur, Behnam / Kim, Solomon / Hutter, Adrian / Agrawal, Priyanka / Castro-Ros, Alex / Driessche, George van den / Wang, Tao / Yang, Fan / Chang, Shuo-yiin / Komarek, Paul / McIlroy, Ross / Lučić, Mario / Zhang, Guodong / Farhan, Wael / Sharman, Michael / Natsev, Paul / Michel, Paul / Cheng, Yong / Bansal, Yamini / Qiao, Siyuan / Cao, Kris / Shakeri, Siamak / Butterfield, Christina / Chung, Justin / Rubenstein, Paul Kishan / Agrawal, Shivani / Mensch, Arthur / Soparkar, Kedar / Lenc, Karel / Chung, Timothy / Pope, Aedan / Maggiore, Loren / Kay, Jackie / Jhakra, Priya / Wang, Shibo / Maynez, Joshua / Phuong, Mary / Tobin, Taylor / Tacchetti, Andrea / Trebacz, Maja / Robinson, Kevin / Katariya, Yash / Riedel, Sebastian / Bailey, Paige / Xiao, Kefan / Ghelani, Nimesh / Aroyo, Lora / Slone, Ambrose / Houlsby, Neil / Xiong, Xuehan / Yang, Zhen / Gribovskaya, Elena / Adler, Jonas / Wirth, Mateo / Lee, Lisa / Li, Music / Kagohara, Thais / Pavagadhi, Jay / Bridgers, Sophie / Bortsova, Anna / Ghemawat, Sanjay / Ahmed, Zafarali / Liu, Tianqi / Powell, Richard / Bolina, Vijay / Iinuma, Mariko / Zablotskaia, Polina / Besley, James / Chung, Da-Woon / Dozat, Timothy / Comanescu, Ramona / Si, Xiance / Greer, Jeremy / Su, Guolong / Polacek, Martin / Kaufman, Raphaël Lopez / Tokumine, Simon / Hu, Hexiang / Buchatskaya, Elena / Miao, Yingjie / Elhawaty, Mohamed / Siddhant, Aditya / Tomasev, Nenad / Xing, Jinwei / Greer, Christina / Miller, Helen / Ashraf, Shereen / Roy, Aurko / Zhang, Zizhao / Ma, Ada / Filos, Angelos / Besta, Milos / Blevins, Rory / Klimenko, Ted / Yeh, Chih-Kuan / Changpinyo, Soravit / Mu, Jiaqi / Chang, Oscar / Pajarskas, Mantas / Muir, Carrie / Cohen, Vered / Lan, Charline Le / Haridasan, Krishna / Marathe, Amit / Hansen, Steven / Douglas, Sholto / Samuel, Rajkumar / Wang, Mingqiu / Austin, Sophia / Lan, Chang / Jiang, Jiepu / Chiu, Justin / Lorenzo, Jaime Alonso / Sjösund, Lars Lowe / Cevey, Sébastien / Gleicher, Zach / Avrahami, Thi / Boral, Anudhyan / Srinivasan, Hansa / Selo, Vittorio / May, Rhys / Aisopos, Konstantinos / Hussenot, Léonard / Soares, Livio Baldini / Baumli, Kate / Chang, Michael B. / Recasens, Adrià / Caine, Ben / Pritzel, Alexander / Pavetic, Filip / Pardo, Fabio / Gergely, Anita / Frye, Justin / Ramasesh, Vinay / Horgan, Dan / Badola, Kartikeya / Kassner, Nora / Roy, Subhrajit / Dyer, Ethan / Campos, Víctor / Tomala, Alex / Tang, Yunhao / Badawy, Dalia El / White, Elspeth / Mustafa, Basil / Lang, Oran / Jindal, Abhishek / Vikram, Sharad / Gong, Zhitao / Caelles, Sergi / Hemsley, Ross / Thornton, Gregory / Feng, Fangxiaoyu / Stokowiec, Wojciech / Zheng, Ce / Thacker, Phoebe / Ünlü, Çağlar / Zhang, Zhishuai / Saleh, Mohammad / Svensson, James / Bileschi, Max / Patil, Piyush / Anand, Ankesh / Ring, Roman / Tsihlas, Katerina / Vezer, Arpi / Selvi, Marco / Shevlane, Toby / Rodriguez, Mikel / Kwiatkowski, Tom / Daruki, Samira / Rong, Keran / Dafoe, Allan / FitzGerald, Nicholas / Gu-Lemberg, Keren / Khan, Mina / Hendricks, Lisa Anne / Pellat, Marie / Feinberg, Vladimir / Cobon-Kerr, James / Sainath, Tara / Rauh, Maribeth / Hashemi, Sayed Hadi / Ives, Richard / Hasson, Yana / Li, YaGuang / Noland, Eric / Cao, Yuan / Byrd, Nathan / Hou, Le / Wang, Qingze / Sottiaux, Thibault / Paganini, Michela / Lespiau, Jean-Baptiste / Moufarek, Alexandre / Hassan, Samer / Shivakumar, Kaushik / van Amersfoort, Joost / Mandhane, Amol / Joshi, Pratik / Goyal, Anirudh / Tung, Matthew / Brock, Andrew / Sheahan, Hannah / Misra, Vedant / Li, Cheng / Rakićević, Nemanja / Dehghani, Mostafa / Liu, Fangyu / Mittal, Sid / Oh, Junhyuk / Noury, Seb / Sezener, Eren / Huot, Fantine / Lamm, Matthew / De Cao, Nicola / Chen, Charlie / Elsayed, Gamaleldin / Chi, Ed / Mahdieh, Mahdis / Tenney, Ian / Hua, Nan / Petrychenko, Ivan / Kane, Patrick / Scandinaro, Dylan / Jain, Rishub / Uesato, Jonathan / Datta, Romina / Sadovsky, Adam / Bunyan, Oskar / Rabiej, Dominik / Wu, Shimu / Zhang, John / Vasudevan, Gautam / Leurent, Edouard / Alnahlawi, Mahmoud / Georgescu, Ionut / Wei, Nan / Zheng, Ivy / Chan, Betty / Rabinovitch, Pam G / Stanczyk, Piotr / Zhang, Ye / Steiner, David / Naskar, Subhajit / Azzam, Michael / Johnson, Matthew / Paszke, Adam / Chiu, Chung-Cheng / Elias, Jaume Sanchez / Mohiuddin, Afroz / Muhammad, Faizan / Miao, Jin / Lee, Andrew / Vieillard, Nino / Potluri, Sahitya / Park, Jane / Davoodi, Elnaz / Zhang, Jiageng / Stanway, Jeff / Garmon, Drew / Karmarkar, Abhijit / Dong, Zhe / Lee, Jong / Kumar, Aviral / Zhou, Luowei / Evens, Jonathan / Isaac, William / Chen, Zhe / Jia, Johnson / Levskaya, Anselm / Zhu, Zhenkai / Gorgolewski, Chris / Grabowski, Peter / Mao, Yu / Magni, Alberto / Yao, Kaisheng / Snaider, Javier / Casagrande, Norman / Suganthan, Paul / Palmer, Evan / Irving, Geoffrey / Loper, Edward / Faruqui, Manaal / Arkatkar, Isha / Chen, Nanxin / Shafran, Izhak / Fink, Michael / Castaño, Alfonso / Giannoumis, Irene / Kim, Wooyeol / Rybiński, Mikołaj / Sreevatsa, Ashwin / Prendki, Jennifer / Soergel, David / Goedeckemeyer, Adrian / Gierke, Willi / Jafari, Mohsen / Gaba, Meenu / Wiesner, Jeremy / Wright, Diana Gage / Wei, Yawen / Vashisht, Harsha / Kulizhskaya, Yana / Hoover, Jay / Le, Maigo / Li, Lu / Iwuanyanwu, Chimezie / Liu, Lu / Ramirez, Kevin / Khorlin, Andrey / Cui, Albert / LIN, Tian / Georgiev, Marin / Wu, Marcus / Aguilar, Ricardo / Pallo, Keith / Chakladar, Abhishek / Repina, Alena / Wu, Xihui / van der Weide, Tom / Ponnapalli, Priya / Kaplan, Caroline / Simsa, Jiri / Li, Shuangfeng / Dousse, Olivier / Piper, Jeff / Ie, Nathan / Lui, Minnie / Pasumarthi, Rama / Lintz, Nathan / Vijayakumar, Anitha / Thiet, Lam Nguyen / Andor, Daniel / Valenzuela, Pedro / Paduraru, Cosmin / Peng, Daiyi / Lee, Katherine / Zhang, Shuyuan / Greene, Somer / Nguyen, Duc Dung / Kurylowicz, Paula / Velury, Sarmishta / Krause, Sebastian / Hardin, Cassidy / Dixon, Lucas / Janzer, Lili / Choo, Kiam / Feng, Ziqiang / Zhang, Biao / Singhal, Achintya / Latkar, Tejasi / Zhang, Mingyang / Le, Quoc / Abellan, Elena Allica / Du, Dayou / McKinnon, Dan / Antropova, Natasha / Bolukbasi, Tolga / Keller, Orgad / Reid, David / Finchelstein, Daniel / Raad, Maria Abi / Crocker, Remi / Hawkins, Peter / Dadashi, Robert / Gaffney, Colin / Lall, Sid / Franko, Ken / Filonov, Egor / Bulanova, Anna / Leblond, Rémi / Yadav, Vikas / Chung, Shirley / Askham, Harry / Cobo, Luis C. / Xu, Kelvin / Fischer, Felix / Xu, Jun / Sorokin, Christina / Alberti, Chris / Lin, Chu-Cheng / Evans, Colin / Zhou, Hao / Dimitriev, Alek / Forbes, Hannah / Banarse, Dylan / Tung, Zora / Liu, Jeremiah / Omernick, Mark / Bishop, Colton / Kumar, Chintu / Sterneck, Rachel / Foley, Ryan / Jain, Rohan / Mishra, Swaroop / Xia, Jiawei / Bos, Taylor / Cideron, Geoffrey / Amid, Ehsan / Piccinno, Francesco / Wang, Xingyu / Banzal, Praseem / Gurita, Petru / Noga, Hila / Shah, Premal / Mankowitz, Daniel J. / Polozov, Alex / Kushman, Nate / Krakovna, Victoria / Brown, Sasha / Bateni, MohammadHossein / Duan, Dennis / Firoiu, Vlad / Thotakuri, Meghana / Natan, Tom / Mohananey, Anhad / Geist, Matthieu / Mudgal, Sidharth / Girgin, Sertan / Li, Hui / Ye, Jiayu / Roval, Ofir / Tojo, Reiko / Kwong, Michael / Lee-Thorp, James / Yew, Christopher / Yuan, Quan / Bagri, Sumit / Sinopalnikov, Danila / Ramos, Sabela / Mellor, John / Sharma, Abhishek / Severyn, Aliaksei / Lai, Jonathan / Wu, Kathy / Cheng, Heng-Tze / Miller, David / Sonnerat, Nicolas / Vnukov, Denis / Greig, Rory / Beattie, Jennifer / Caveness, Emily / Bai, Libin / Eisenschlos, Julian / Korchemniy, Alex / Tsai, Tomy / Jasarevic, Mimi / Kong, Weize / Dao, Phuong / Zheng, Zeyu / Liu, Frederick / Zhu, Rui / Geller, Mark / Teh, Tian Huey / 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    A Family of Highly Capable Multimodal Models

    2023  

    Abstract: This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from ... ...

    Abstract This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of Gemini models in cross-modal reasoning and language understanding will enable a wide variety of use cases and we discuss our approach toward deploying them responsibly to users.
    Keywords Computer Science - Computation and Language ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition
    Subject code 004
    Publishing date 2023-12-18
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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