Zobrazeno 1 - 10
of 37
pro vyhledávání: '"BANBURY, COLBY"'
Autor:
Chen, Tianyi, Qu, Xiaoyi, Aponte, David, Banbury, Colby, Ko, Jongwoo, Ding, Tianyu, Ma, Yong, Lyapunov, Vladimir, Zharkov, Ilya, Liang, Luming
Structured pruning is one of the most popular approaches to effectively compress the heavy deep neural networks (DNNs) into compact sub-networks while retaining performance. The existing methods suffer from multi-stage procedures along with significa
Externí odkaz:
http://arxiv.org/abs/2409.09085
Autor:
Banbury, Colby, Njor, Emil, Garavagno, Andrea Mattia, Stewart, Matthew, Warden, Pete, Kudlur, Manjunath, Jeffries, Nat, Fafoutis, Xenofon, Reddi, Vijay Janapa
Tiny machine learning (TinyML) for low-power devices lacks robust datasets for development. We present Wake Vision, a large-scale dataset for person detection that contains over 6 million quality-filtered images. We provide two variants: Wake Vision
Externí odkaz:
http://arxiv.org/abs/2405.00892
Autor:
Qin, Danfeng, Leichner, Chas, Delakis, Manolis, Fornoni, Marco, Luo, Shixin, Yang, Fan, Wang, Weijun, Banbury, Colby, Ye, Chengxi, Akin, Berkin, Aggarwal, Vaibhav, Zhu, Tenghui, Moro, Daniele, Howard, Andrew
We present the latest generation of MobileNets, known as MobileNetV4 (MNv4), featuring universally efficient architecture designs for mobile devices. At its core, we introduce the Universal Inverted Bottleneck (UIB) search block, a unified and flexib
Externí odkaz:
http://arxiv.org/abs/2404.10518
Autor:
Prakash, Shvetank, Stewart, Matthew, Banbury, Colby, Mazumder, Mark, Warden, Pete, Plancher, Brian, Reddi, Vijay Janapa
The sustained growth of carbon emissions and global waste elicits significant sustainability concerns for our environment's future. The growing Internet of Things (IoT) has the potential to exacerbate this issue. However, an emerging area known as Ti
Externí odkaz:
http://arxiv.org/abs/2301.11899
Autor:
Kwon, Hyoukjun, Nair, Krishnakumar, Seo, Jamin, Yik, Jason, Mohapatra, Debabrata, Zhan, Dongyuan, Song, Jinook, Capak, Peter, Zhang, Peizhao, Vajda, Peter, Banbury, Colby, Mazumder, Mark, Lai, Liangzhen, Sirasao, Ashish, Krishna, Tushar, Khaitan, Harshit, Chandra, Vikas, Reddi, Vijay Janapa
Real-time multi-task multi-model (MTMM) workloads, a new form of deep learning inference workloads, are emerging for applications areas like extended reality (XR) to support metaverse use cases. These workloads combine user interactivity with computa
Externí odkaz:
http://arxiv.org/abs/2211.08675
Autor:
Hymel, Shawn, Banbury, Colby, Situnayake, Daniel, Elium, Alex, Ward, Carl, Kelcey, Mat, Baaijens, Mathijs, Majchrzycki, Mateusz, Plunkett, Jenny, Tischler, David, Grande, Alessandro, Moreau, Louis, Maslov, Dmitry, Beavis, Artie, Jongboom, Jan, Reddi, Vijay Janapa
Edge Impulse is a cloud-based machine learning operations (MLOps) platform for developing embedded and edge ML (TinyML) systems that can be deployed to a wide range of hardware targets. Current TinyML workflows are plagued by fragmented software stac
Externí odkaz:
http://arxiv.org/abs/2212.03332
Autor:
Mazumder, Mark, Banbury, Colby, Yao, Xiaozhe, Karlaš, Bojan, Rojas, William Gaviria, Diamos, Sudnya, Diamos, Greg, He, Lynn, Parrish, Alicia, Kirk, Hannah Rose, Quaye, Jessica, Rastogi, Charvi, Kiela, Douwe, Jurado, David, Kanter, David, Mosquera, Rafael, Ciro, Juan, Aroyo, Lora, Acun, Bilge, Chen, Lingjiao, Raje, Mehul Smriti, Bartolo, Max, Eyuboglu, Sabri, Ghorbani, Amirata, Goodman, Emmett, Inel, Oana, Kane, Tariq, Kirkpatrick, Christine R., Kuo, Tzu-Sheng, Mueller, Jonas, Thrush, Tristan, Vanschoren, Joaquin, Warren, Margaret, Williams, Adina, Yeung, Serena, Ardalani, Newsha, Paritosh, Praveen, Bat-Leah, Lilith, Zhang, Ce, Zou, James, Wu, Carole-Jean, Coleman, Cody, Ng, Andrew, Mattson, Peter, Reddi, Vijay Janapa
Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithfulness of the underlying problems. Neglecting the fundamental importan
Externí odkaz:
http://arxiv.org/abs/2207.10062
Autor:
Warden, Pete, Stewart, Matthew, Plancher, Brian, Banbury, Colby, Prakash, Shvetank, Chen, Emma, Asgar, Zain, Katti, Sachin, Reddi, Vijay Janapa
Machine learning sensors represent a paradigm shift for the future of embedded machine learning applications. Current instantiations of embedded machine learning (ML) suffer from complex integration, lack of modularity, and privacy and security conce
Externí odkaz:
http://arxiv.org/abs/2206.03266
Autor:
Neuman, Sabrina M., Plancher, Brian, Duisterhof, Bardienus P., Krishnan, Srivatsan, Banbury, Colby, Mazumder, Mark, Prakash, Shvetank, Jabbour, Jason, Faust, Aleksandra, de Croon, Guido C. H. E., Reddi, Vijay Janapa
Machine learning (ML) has become a pervasive tool across computing systems. An emerging application that stress-tests the challenges of ML system design is tiny robot learning, the deployment of ML on resource-constrained low-cost autonomous robots.
Externí odkaz:
http://arxiv.org/abs/2205.05748
Autor:
Prakash, Shvetank, Callahan, Tim, Bushagour, Joseph, Banbury, Colby, Green, Alan V., Warden, Pete, Ansell, Tim, Reddi, Vijay Janapa
Publikováno v:
IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS). (2023) 157-167
Need for the efficient processing of neural networks has given rise to the development of hardware accelerators. The increased adoption of specialized hardware has highlighted the need for more agile design flows for hardware-software co-design and d
Externí odkaz:
http://arxiv.org/abs/2201.01863