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
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
:github: Possibly-Smallest ESP32 Board uses Smallest-Footprint Parts.
Whenever there’s a superlative involved, you know that degree of optimization has to leave something else on the table. In the case of [PegorK]’s f32, the smallest ESP32 dev board I’ve seen, the cost of miniaturization is GPIO.
https://github.com/PegorK/f32?tab=readme-ov-file
#f32 #miniaturization #esp32 #dev #board #diy #engineering #media #tech #art #news
![Aside from leaving out GPIO and needing too-small SMD components, [Pegor] admits that pesky little details like antenna matching circuits and decoupling capacitors had to get cut to make the tiny footprint, so this board might be more of a stunt than anything practical.
👾So what can you do with the smallest ESP32 board? Well, [Pegor] put up a basic web interface up to get you started blinking the built-in LED; after that, it’s up to you.👾](https://files.mastodon.social/cache/media_attachments/files/115/627/865/852/965/362/original/3f69d11f3b4a2da9.jpeg)
![[ImageSource: PegorK]
⁉️There’s only one GPIO pin broken out, and it’s pre-wired to an LED. That’s the bad news, and depending on what you want an ESP32 for, it might not phase you at all. What is impressive here, if not the number of I/O pins, is the size of the board: at 9.85 mm x 8.45 mm barely overhangs the USB-C socket that takes up one side of the board.⁉️](https://files.mastodon.social/cache/media_attachments/files/115/627/865/886/711/443/original/361eb402a6f40652.jpeg)
![[ImageSource: PegorK]
[Pegor] provides this helpful image in the readme so you know what you’re getting into with the 01005 resistors.
👾In order to get the ESP32-C3FH4 onto such a tiny board, all of the other support hardware had to be the smallest possible sizes — including resistors in 01005. If you don’t speak SMD, one could read that number code as “oh god too small” — at 0.4 mm x 0.2 mm it’s as minuscule as you’ll find– and [Pegor] hand soldered them.👾](https://files.mastodon.social/cache/media_attachments/files/115/627/865/913/288/709/original/d8ab3414b0557d41.jpeg)
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
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
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/2410.01799
arXiv: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.
My #F32 resub application to study #TCR repertoire changes after #AAV #genetherapy was...
Not discussed 🫠 #immunology
Me closing eRA commons:
Fedora 32 is officially here!
https://fedoramagazine.org/announcing-fedora-32/
