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of 6
pro vyhledávání: '"Jeffries, Nat"'
This paper introduces Moonshine, a family of speech recognition models optimized for live transcription and voice command processing. Moonshine is based on an encoder-decoder transformer architecture and employs Rotary Position Embedding (RoPE) inste
Externí odkaz:
http://arxiv.org/abs/2410.15608
Autor:
Banbury, Colby, Njor, Emil, Stewart, Matthew, Warden, Pete, Kudlur, Manjunath, Jeffries, Nat, Fafoutis, Xenofon, Reddi, Vijay Janapa
Tiny machine learning (TinyML), which enables machine learning applications on extremely low-power devices, suffers from limited size and quality of relevant datasets. To address this issue, we introduce Wake Vision, a large-scale, diverse dataset ta
Externí odkaz:
http://arxiv.org/abs/2405.00892
Autor:
Stewart, Matthew, Warden, Pete, Omri, Yasmine, Prakash, Shvetank, Santos, Joao, Hymel, Shawn, Brown, Benjamin, MacArthur, Jim, Jeffries, Nat, Katti, Sachin, Plancher, Brian, Reddi, Vijay Janapa
Machine learning (ML) sensors are enabling intelligence at the edge by empowering end-users with greater control over their data. ML sensors offer a new paradigm for sensing that moves the processing and analysis to the device itself rather than rely
Externí odkaz:
http://arxiv.org/abs/2306.08848
Autor:
Banbury, Colby, Reddi, Vijay Janapa, Torelli, Peter, Holleman, Jeremy, Jeffries, Nat, Kiraly, Csaba, Montino, Pietro, Kanter, David, Ahmed, Sebastian, Pau, Danilo, Thakker, Urmish, Torrini, Antonio, Warden, Peter, Cordaro, Jay, Di Guglielmo, Giuseppe, Duarte, Javier, Gibellini, Stephen, Parekh, Videet, Tran, Honson, Tran, Nhan, Wenxu, Niu, Xuesong, Xu
Advancements in ultra-low-power tiny machine learning (TinyML) systems promise to unlock an entirely new class of smart applications. However, continued progress is limited by the lack of a widely accepted and easily reproducible benchmark for these
Externí odkaz:
http://arxiv.org/abs/2106.07597
Autor:
David, Robert, Duke, Jared, Jain, Advait, Reddi, Vijay Janapa, Jeffries, Nat, Li, Jian, Kreeger, Nick, Nappier, Ian, Natraj, Meghna, Regev, Shlomi, Rhodes, Rocky, Wang, Tiezhen, Warden, Pete
Deep learning inference on embedded devices is a burgeoning field with myriad applications because tiny embedded devices are omnipresent. But we must overcome major challenges before we can benefit from this opportunity. Embedded processors are sever
Externí odkaz:
http://arxiv.org/abs/2010.08678
Autor:
Stewart, Matthew, Warden, Pete, Omri, Yasmine, Prakash, Shvetank, Santos, Joao, Hymel, Shawn, Brown, Benjamin, MacArthur, Jim, Jeffries, Nat, Plancher, Brian, Reddi, Vijay Janapa
Machine learning (ML) sensors offer a new paradigm for sensing that enables intelligence at the edge while empowering end-users with greater control of their data. As these ML sensors play a crucial role in the development of intelligent devices, cle
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::681165c7db0be850a20d9233a477ee8d