#f32 — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #f32, aggregated by home.social.
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Technische Forensiek - Daan & Team: Nefit Bosch
Nefit Bosch Compress 5800i: Storing F32 Persgastemperatuursensor
#NefitBosch #Compress5800i #F32
🔍 Volledig Rapport: https://www.wpstoring.org/nefit-bosch/compress-5800i/nefit-bosch-compress-5800i-storing-f32-persgastemperatuursensor
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System Diagnostic by Marcus: EG4
EG4 18KPV: Fault Code F32 - M3 Microprocessor Rx Failure
🔍 Full Report: https://www.storagefaults.com/eg4/18kpv/eg4-18kpv-fault-code-f32-microprocessor-rx-failure
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Ah, behold the revolutionary #F32, an #ESP32 board so microscopic that losing it is as easy as losing interest while reading about it. 🤏✨ #GitHub adds more buttons and #AI fluff to keep you busy while you wonder why you ever cared. 🙄🚀
https://github.com/PegorK/f32 #technology #innovation #microelectronics #HackerNews #ngated -
Ah, behold the revolutionary #F32, an #ESP32 board so microscopic that losing it is as easy as losing interest while reading about it. 🤏✨ #GitHub adds more buttons and #AI fluff to keep you busy while you wonder why you ever cared. 🙄🚀
https://github.com/PegorK/f32 #technology #innovation #microelectronics #HackerNews #ngated -
Ah, behold the revolutionary #F32, an #ESP32 board so microscopic that losing it is as easy as losing interest while reading about it. 🤏✨ #GitHub adds more buttons and #AI fluff to keep you busy while you wonder why you ever cared. 🙄🚀
https://github.com/PegorK/f32 #technology #innovation #microelectronics #HackerNews #ngated -
Ah, behold the revolutionary #F32, an #ESP32 board so microscopic that losing it is as easy as losing interest while reading about it. 🤏✨ #GitHub adds more buttons and #AI fluff to keep you busy while you wonder why you ever cared. 🙄🚀
https://github.com/PegorK/f32 #technology #innovation #microelectronics #HackerNews #ngated -
F32 – An Extremely Small ESP32 Board
#HackerNews #F32 #ESP32 #Small #Board #IoT #Technology #Electronics #Innovation
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F32 – An Extremely Small ESP32 Board
#HackerNews #F32 #ESP32 #Small #Board #IoT #Technology #Electronics #Innovation
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F32 – An Extremely Small ESP32 Board
#HackerNews #F32 #ESP32 #Small #Board #IoT #Technology #Electronics #Innovation
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F32 – An Extremely Small ESP32 Board
#HackerNews #F32 #ESP32 #Small #Board #IoT #Technology #Electronics #Innovation
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F32 – An Extremely Small ESP32 Board
#HackerNews #F32 #ESP32 #Small #Board #IoT #Technology #Electronics #Innovation
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Katharinenhospital Stuttgart: Heizung defekt – Patient aus Weinstadt friert lang – Nachrichten aus Weinstadt
Weinstadt/St…
#Stuttgart #Deutschland #Deutsch #DE #Schlagzeilen #Headlines #Nachrichten #News #Europe #Europa #EU #AnnetteSeifert #Baden-Württemberg #F32 #Frieren #Germany #heizlüfter #Jacke #Kälte #Katharinenhospital #KlinikumStuttgart #Krankenhaus #MikeRauschenberger #mobilesHeizgerät #Patient #Station #Temperaturen #Weinstadt #Winter #Winterjacke
https://www.europesays.com/de/585102/ -
OLED Rückleuchten für den BMW| EINBAU | BMW F36
https://themotorbikechannel.com/oled-ruckleuchten-fur-den-bmw-einbau-bmw-f36/?feed_id=14256&_unique_id=671045b82395e
Source: OLED Rückleuchten für den BMW| E...#3er #418d #420d #420i #425d #425i #430d #430i #435i #440i #4er #anleitung #auto #autoteile #bmw #BMWoled #dm #Einbau #einbauen #erfahrung #f30 #f31 #f32 #f33 #f34 #f36 #oled #reperatur #review #rückleuchte #vland #wechseln
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Efficient $1$-bit tensor approximations
Alex W. Neal Riasanovsky, Sarah El Kazdadi
https://arxiv.org/abs/2410.01799 https://arxiv.org/pdf/2410.01799 https://arxiv.org/html/2410.01799arXiv:2410.01799v1 Announce Type: new
Abstract: We present a spatially efficient decomposition of matrices and arbitrary-order tensors as linear combinations of tensor products of $\{-1, 1\}$-valued vectors. For any matrix $A \in \mathbb{R}^{m \times n}$, $$A - R_w = S_w C_w T_w^\top = \sum_{j=1}^w c_j \cdot \mathbf{s}_j \mathbf{t}_j^\top$$ is a {\it $w$-width signed cut decomposition of $A$}. Here $C_w = "diag"(\mathbf{c}_w)$ for some $\mathbf{c}_w \in \mathbb{R}^w,$ and $S_w, T_w$, and the vectors $\mathbf{s}_j, \mathbf{t}_j$ are $\{-1, 1\}$-valued. To store $(S_w, T_w, C_w)$, we may pack $w \cdot (m + n)$ bits, and require only $w$ floating point numbers. As a function of $w$, $\|R_w\|_F$ exhibits exponential decay when applied to #f32 matrices with i.i.d. $\mathcal N (0, 1)$ entries. Choosing $w$ so that $(S_w, T_w, C_w)$ has the same memory footprint as a \textit{f16} or \textit{bf16} matrix, the relative error is comparable. Our algorithm yields efficient signed cut decompositions in $20$ lines of pseudocode. It reflects a simple modification from a celebrated 1999 paper [1] of Frieze and Kannan. As a first application, we approximate the weight matrices in the open \textit{Mistral-7B-v0.1} Large Language Model to a $50\%$ spatial compression. Remarkably, all $226$ remainder matrices have a relative error $<6\%$ and the expanded model closely matches \textit{Mistral-7B-v0.1} on the {\it huggingface} leaderboard [2]. Benchmark performance degrades slowly as we reduce the spatial compression from $50\%$ to $25\%$. We optimize our open source \textit{rust} implementation [3] with \textit{simd} instructions on \textit{avx2} and \textit{avx512} architectures. We also extend our algorithm from matrices to tensors of arbitrary order and use it to compress a picture of the first author's cat Angus. -
Efficient $1$-bit tensor approximations
Alex W. Neal Riasanovsky, Sarah El Kazdadi
https://arxiv.org/abs/2410.01799 https://arxiv.org/pdf/2410.01799 https://arxiv.org/html/2410.01799arXiv:2410.01799v1 Announce Type: new
Abstract: We present a spatially efficient decomposition of matrices and arbitrary-order tensors as linear combinations of tensor products of $\{-1, 1\}$-valued vectors. For any matrix $A \in \mathbb{R}^{m \times n}$, $$A - R_w = S_w C_w T_w^\top = \sum_{j=1}^w c_j \cdot \mathbf{s}_j \mathbf{t}_j^\top$$ is a {\it $w$-width signed cut decomposition of $A$}. Here $C_w = "diag"(\mathbf{c}_w)$ for some $\mathbf{c}_w \in \mathbb{R}^w,$ and $S_w, T_w$, and the vectors $\mathbf{s}_j, \mathbf{t}_j$ are $\{-1, 1\}$-valued. To store $(S_w, T_w, C_w)$, we may pack $w \cdot (m + n)$ bits, and require only $w$ floating point numbers. As a function of $w$, $\|R_w\|_F$ exhibits exponential decay when applied to #f32 matrices with i.i.d. $\mathcal N (0, 1)$ entries. Choosing $w$ so that $(S_w, T_w, C_w)$ has the same memory footprint as a \textit{f16} or \textit{bf16} matrix, the relative error is comparable. Our algorithm yields efficient signed cut decompositions in $20$ lines of pseudocode. It reflects a simple modification from a celebrated 1999 paper [1] of Frieze and Kannan. As a first application, we approximate the weight matrices in the open \textit{Mistral-7B-v0.1} Large Language Model to a $50\%$ spatial compression. Remarkably, all $226$ remainder matrices have a relative error $<6\%$ and the expanded model closely matches \textit{Mistral-7B-v0.1} on the {\it huggingface} leaderboard [2]. Benchmark performance degrades slowly as we reduce the spatial compression from $50\%$ to $25\%$. We optimize our open source \textit{rust} implementation [3] with \textit{simd} instructions on \textit{avx2} and \textit{avx512} architectures. We also extend our algorithm from matrices to tensors of arbitrary order and use it to compress a picture of the first author's cat Angus. -
Efficient $1$-bit tensor approximations
Alex W. Neal Riasanovsky, Sarah El Kazdadi
https://arxiv.org/abs/2410.01799 https://arxiv.org/pdf/2410.01799 https://arxiv.org/html/2410.01799arXiv:2410.01799v1 Announce Type: new
Abstract: We present a spatially efficient decomposition of matrices and arbitrary-order tensors as linear combinations of tensor products of $\{-1, 1\}$-valued vectors. For any matrix $A \in \mathbb{R}^{m \times n}$, $$A - R_w = S_w C_w T_w^\top = \sum_{j=1}^w c_j \cdot \mathbf{s}_j \mathbf{t}_j^\top$$ is a {\it $w$-width signed cut decomposition of $A$}. Here $C_w = "diag"(\mathbf{c}_w)$ for some $\mathbf{c}_w \in \mathbb{R}^w,$ and $S_w, T_w$, and the vectors $\mathbf{s}_j, \mathbf{t}_j$ are $\{-1, 1\}$-valued. To store $(S_w, T_w, C_w)$, we may pack $w \cdot (m + n)$ bits, and require only $w$ floating point numbers. As a function of $w$, $\|R_w\|_F$ exhibits exponential decay when applied to #f32 matrices with i.i.d. $\mathcal N (0, 1)$ entries. Choosing $w$ so that $(S_w, T_w, C_w)$ has the same memory footprint as a \textit{f16} or \textit{bf16} matrix, the relative error is comparable. Our algorithm yields efficient signed cut decompositions in $20$ lines of pseudocode. It reflects a simple modification from a celebrated 1999 paper [1] of Frieze and Kannan. As a first application, we approximate the weight matrices in the open \textit{Mistral-7B-v0.1} Large Language Model to a $50\%$ spatial compression. Remarkably, all $226$ remainder matrices have a relative error $<6\%$ and the expanded model closely matches \textit{Mistral-7B-v0.1} on the {\it huggingface} leaderboard [2]. Benchmark performance degrades slowly as we reduce the spatial compression from $50\%$ to $25\%$. We optimize our open source \textit{rust} implementation [3] with \textit{simd} instructions on \textit{avx2} and \textit{avx512} architectures. We also extend our algorithm from matrices to tensors of arbitrary order and use it to compress a picture of the first author's cat Angus. -
Fedora 32 is officially here!
https://fedoramagazine.org/announcing-fedora-32/ -
#Fedora32 is officially here! - #Fedora Magazine
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#Fedora32 is officially here! - #Fedora Magazine