Semiconductor stocks especially linked to artificial intelligence led by Nvidia Corp (NASDAQ:NVDA), Advanced Micro Devices, Inc (NASDAQ:AMD), Intel Corp (NASDAQ:INTC), Broadcom Inc (NASDAQ:AVGO), Taiwan Semiconductor Manufacturing Co (NYSE:TSM), Arm Holdings Plc (NASDAQ:ARM), Qualcomm Inc ...

What's Going On With Nvidia, Braodcom, Intel, And Other Chip Stocks On Thursday?

Apollo Global Management Inc. (NYSE:APO) has reportedly been considering a multibillion-dollar investment in Intel Corp. (NASDAQ:INTC). What Happened: Apollo has proposed to make an equity-like investment of up to $5 billion in Intel, reported ...

Intel Has A New Knight In Shining Armor? After Qualcomm, Apollo Global Management Reportedly Proposes Multibillion-Dollar Investment

The “massive” deal, as the authors put it, is financially daunting, as Qualcomm has just $13 billion in cash and equivalents on its balance sheet against $13 billion of long-term debt. Even a mostly stock exchange would require some large debt raise.

Qualcomm wants to buy Intel. Would that be enough to overtake Nvidia?

By using Samsung network hardware and a Qualcomm modem. This tech should be commercially available by the end of the year.

Samsung and Qualcomm demonstrate 20% speed boost to 5G - GSMArena.com news

Early details of the ThinkPad series&#x27; return to Qualcomm Snapdragon chipsets have surfaced online. Seemingly arriving as the ThinkPad T14s Gen 6, the laptop is expected to be one of the first to showcase Qualcomm&#x27;s long-awaited Apple M3 rival.

Lenovo ThinkPad T14s Gen 6 with Qualcomm Snapdragon X Elite leaks prior to official release - NotebookCheck.net News

Qualcomm has only showcased Snapdragon X Elite reference devices to date. However, a reliable leaker has now provided an early look at the Yoga Slim 7 Snapdragon Edition, which shares its design with the x86 version of the same laptop, but with one unusual omission.

Leaks reveals Lenovo Yoga Slim 7 Snapdragon Edition full Qualcomm Snapdragon X Elite release without 3.5 mm jack - NotebookCheck.net News

Time to check the current stand of things and compare Qualcomm&#x27;s first real Windows-on-ARM opportunity against Intel, AMD and Apple in a benchmark test with the current high-end chips of 2024. Qualcomm also makes new promises in terms of efficiency, battery life and app availability.

News of the Snapdragon X Elite: Full steam ahead to laptop dominance over Intel, AMD and Apple M3 (video) - NotebookCheck.net News

Companies increased downlink speeds over 20% by using the latest 1024 QAM technology

Samsung and Qualcomm Achieve Innovative Industry-First Milestone With Advanced Modulation Technology – Samsung Global Newsroom

Qualcomm

qualcomm

has always conquered the world of smartphones and androids since its day of the first release. The evolution of smartphone processor has always been through the hands of Qualcomm Snapdragon. Not only smartphones it has also powered many modern technologies to the next level. Now Qualcomm Snapdragon is stepping towards the future of with the help the latest Snapdragon processor, Snapdragon 855 and Snapdragon 8cx. Qualcomm technology Lets talk about first……….. Snapdragon 855 The first processor to support To explain 5G in simple, it is the successor of 4G and promises to provide uninterrupted high-speed internet up to (Gbps). The snapdragon 855 delivers transformable 5G experience. . It comes with Qualcomm Snapdragon X50 Modem. 5G. 20 Gigabits per second It is worlds first 5G mmWave and sub-6 GHz mobile solution and World’s first 2 Gbps LTE modem including the world’s first 7x Carrier Aggregation and world’s first 14nm RF Chip Although snapdragon 855 will not have built in 5G modem and it will use external modem to support 5G. The performance of snapdragon 855 is powered by Basically, we have octa-core processor that have four high performance cores and four energy efficient cores. But there the core distribution is different this time. It has one Primary Core with clock speed up to 2.84 GHz, three Performance Core with clock speed up to 2.42 GHz and four efficiency core with clock speed up to 1.80GHz. These core boost the performance in case of multi-threading or multi-tasking. When there is task which needed to be ended as fast as possible or if there is a single threaded task then Primary core could solve this more easily as it has more clock speed and more cache memory compared to other cores, and when there is a multi-threaded task it would use the performance cores it boost the performance and we also have the energy efficient cores to boost multi-tasking performance. Kryo 485 CPU. In case of GPU also it is not lagging behind. It have the latest . It promises a 20% increase in performance compared to previous snapdragon processors. With the next-gen Adreno 640 GPU, , , and Physically Based Rendering, elite gaming on mobile is achieved. It promises to boost Physically Based Rendering so objects are recreated to mimic the real-life look, feel, depth, and material, presenting extremely realistic, deeply dimensional visuals. It will also give you stable performance for a very long time while gaming and time for app launches will also be reduced. Adreno 640 GPU Vulkan 1.1( First for mobile gaming) HDR Gaming( First true HDR gaming on mobile) Qualcomm snapdragon 855’s A.I. engine also includes , Qualcomm Hexagon Vector Accelerator, Qualcomm Hexagon Tensor Accelerator, Qualcomm Hexagon Voice Assistant, Qualcomm All-Ways Aware technology. Neural networking or neural processing forms the backbone of every A.I. processor and the is in case of snapdragon also, Qualcomm Hexagon 690 Processor boost and improves the neural processing compared to its previous processors. It has 4th generation A.I. engine featuring Tensor Accelerator. It boosts and improves the performance of all the A.I. based activities and security in smartphones. Qualcomm Hexagon 690 Processor World’s First HDR10+ mobile video capture and playback for imagery with even more contrast and detail. World’s First 4K HDR Video Capture with Portrait Mode (Bokeh) so you can film cinema-grade videos. This comes with the Dual 14-bit CV-ISPs, Hardware accelerator for computer vision (CV-ISP), 380 image signal processor. It helps to click high quality photos more efficiently in less time. Here less time means, when you click a high quality picture you need to hold the camera for a while after the click in order to get the perfect image. But with the introduction of the time is reduced and you get a high quality picture instantly after the click with low battery consumption. It also enables 10 bit color depth video capture and Slow motion video capture up to 720p at 480fps. It also provides hardware based Electronic Image Stabilization solution with camera subsystem. World’s First Computer Vision ISP (CV-ISP) delivers real-time object recognition, segmentation, and replacement to allow users to isolate and swap out backgrounds and objects– in real-time. Qualcomm Spectra Computer Vision ISP With this processor, we would be able to experience a complete boost in the performance in the smartphones in terms of gaming, entertainment, internet connectivity, security. There are guesses that Xiaomi Mi Mix 3 5G variant and One Plus six T might have the Snapdragon 855 processor in 2019. Now lets look at Snapdragon 8cx …………… This processor is also know as . This processor is specially built for windows 10 and it is only for PCs. Although Qualcomm develops Snapdragon is only for mobile platforms but its journey is also towards conquering the world of PCs and laptop. For those who don’t know about Qualcomm’s journey in the world of PCs and Laptops please . It is also one of my publication, I hope you would enjoy the article and it would help you to know more about the world of technology. the most Extreme snapdragon ever Click Here Snapdragon is the worlds first 7nm chip for a PC. There is Kyro 495, Adreno 680 and Hexagon 690 for GPU and CPU it allows 2X graphics and 2X 4K monitors. It is 2X faster than its previous generation, Adreno 680 is 60 percent more efficient than the GPU in the Snapdragon 850 Mobile Compute Platform. These GPU components provide premium mobile PC performance without unnecessarily compromising battery life. The Kryo 495 features an octa-core CPU design with larger L2 Cache, L3 Cache, and system Cache. More Caches are available so it would smooth-en the multitasking activities. It also has Spectra 390 and Snapdragon X24 modem( the world’s first announced modem to support Category 20 LTE with speeds of up to 2.0 Gbps.). The integrated Snapdragon X24 modem also allows 90+% of global operators to obtain Gigabit speeds. It supports ultra-fast, 2nd gen USB 3.1 and with 3rd gen PCI-E, you get limitless peripherals and fast storage. The Snapdragon 8cx supports dual 4K HDR displays and cutting-edge Hi-Fi Audio. With a built-in Qualcomm Aqstic audio codec and Qualcomm aptX HD codec. It also supports Qualcomm Quick Charge 4+ technology means you can power back up quickly and get back to being productive. It also ensures high battery life. The Snapdragon 8cx also supports 4X4 MIMO and 7X carrier aggregation that enable 70 percent greater throughput at the edge or in weak or crowded conditions compared to competitor solutions Always On, Always Connected PCs empower a new class of user experiences with their smartphone-like LTE connectivity. This processor would definitely bring a strong competition for Intel and AMD. The most remarkable improvement of this processor is that it could bring high performance in thin, fan-less and light design and it also increases performance for the user in an energy efficient way which gives more battery life to the system. Qualcomm’s Snapdragon will bring a lot of improvement in the world of technology, with that it would also awaken its other competitor in the race of developing smart processors. Will Qualcomm’s Snapdragon prevails with its world’s first title in the world of technology or soon it would be taken by some other company? Till then let’s see how people would accept the latest Qualcomm Snapdragon processors with the worlds first title. Please comment below and let me know your opinion about this topic

Technology meets The Real Deal: Qualcomm’s Snapdragon 855 and Snapdragon 8cx

Comparing the reliability of ARM and Qualcomm Android graphics drivers, S8 to S8 The GraphicsFuzz ShaderTest GLES test suite reveals a variety of graphics driver issues on the Samsung Galaxy S8. The Qualcomm GPU model suffers from considerably more rendering issues and crashes than the ARM GPU model. . Skip to our analysis of the results follow-on posts evaluating: See also: NVIDIA shield Google Pixel devices Hugues Evrard, Paul Thomson and I have recently founded a company, , specializing in automated testing of graphics drivers. The company is the fruit of research at Imperial College London that we described last year through . GraphicsFuzz a series of Medium stories Click above to try out our GraphicsFuzz Android benchmark web app. Our initial product is , a large suite of fragment shaders that GPU designers can license in order to thoroughly test the reliability of OpenGL ES drivers. ShaderTest GLES This is the first in a series of stories where we’re going to look at the reliability of Android ES drivers, as judged by running them on a representative sample of the shaders. OpenGL ShaderTest GLES You can also try out a selection of these shaders on your Android devices via the — try out the web app in your browser and let us know what you find! GraphicsFuzz Android benchmark ShaderTest GLES: testing shader compiler quality We’ll start with some background on ShaderTest GLES, but ! if you are impatient you can skip to the results Traditional GPU test suites focus on rendering speed or API feature-completeness. ShaderTest GLES adds another dimension to the story by testing . After all, a fast rendering result may not be particularly useful if it is wrong! shader compiler quality Talking with software engineers in industry, we have heard time and again that GPU driver developers prioritize testing their drivers against high-profile games and apps— e.g. the top 100 games in the Google Play Store. An unfortunate side-effect of this is that developers of less popular apps must work around driver bugs to make their apps work on consumer devices. Coupled with the slow speed at which Android updates are pushed to end users, this creates a self-perpetuating situation where GPU driver bugs are not seen as a serious issue because popular games run fine (possibly using workarounds), and GPU driver bugs are not worth reporting because it is necessary to work around the bugs anyway if you want consumers to be able to use your app. It also discourages developers from writing interesting shaders: until the driver quality situation improves, they are unlikely to work on a range of devices. The ShaderTest GLES approach ( .) See this post for some background on shaders and shader compilers ShaderTest GLES consists of many . Each family contains one that is used to render a single frame, and several hundred designed to render a visually identical frame to the reference, but to exercise the driver in radically different ways in the process. shader families reference shader variant shaders The variant shaders are obtained from the reference shaders by applying transformations to the source code of the reference shader that should not affect rendering results. For instance, wrapping a block of code in the reference shader in a loop of the form: semantics-preserving transformations — for (int i = 0; i < 1; i++) {// original code from reference shader} is a simple example of a semantics-preserving transformation. ShaderTest GLES finds bugs in graphics drivers by identifying rendering mismatches, errors and crashes, across families of equivalent shaders. ShaderTest ES highlights driver bugs and issues when: a variant shader is by the driver’s shader compiler — a ; rejected compile failure a variant shader causes some component of the driver to — a ; crash crash error a variant shader leads to a noticeably different image being rendered compared with the reference shader — a ; rendering issue a variant shader causes the driver to bail out due to time or memory constraints — a . resource issue and always indicate bugs. usually arise due to errors, whereby the shader compiler generates wrong code; they sometimes also manifest due to floating-point sensitivity in the graphics systems — ShaderTest GLES can shed light on both issues. A big win here is that miscompilations that trigger rendering issues are very difficult to detect and thus often evade traditional testing techniques. provide insights into the nonfunctional constraints of the graphics driver. Compile failures crash errors Rendering issues miscompilation Resource issues To the Samsungs! Let’s kick off by comparing the results for some reference and variant shaders with respect to one of the most popular Android devices on the market: the Samsung Galaxy S8 phone. Depending on territory, the S8 ships with either an ARM or Qualcomm GPU, so let’s see how the drivers from these GPU designers fare. . Our GraphicsFuzz site shows a comparison between the ARM and Qualcomm S8 models across 10 shader families A snapshot of , which compares the ARM and Qualcomm S8s head-to-head. our online results table Comparing results between the ARM and Qualcomm GPU drivers — we’ll simply call them “ARM” and “Qualcomm” from now on — the ARM model comes off looking more reliable overall. We find the following issues: Rendering issues Qualcomm suffers from significantly more : show five rendering issues from Qualcomm compared with one from ARM. rendering issues our tables For example, . Crashes aside, ARM universally renders the reference image: check out the results for shader family 001 The reference image for shader family 001 . All variants should render a similar image. while with Qualcomm we get the reference image for most variants, but a couple of erroneous images in the mix too: On Qualcomm, we get the expected image (left) for most variants in , but two erroneous images (middle and right), likely due to shader compiler bugs. shader family 001 The reference shader for shader is not floating-point-sensitive, so these discrepancies very likely correspond to shader compiler bugs. shader family 001 We also observe one ARM rendering issue, for variant 77 of : shader family 007 Reference image for (left) compared with bad image rendered by variant 77 (right) on the ARM S8. shader family 007 Nondeterminism Our testing revealed two issues on Qualcomm where the driver behaves nondeterministically: sometimes the shader compiler crashes during compilation; other times compilation succeeds and an incorrect image is rendered. This occurs for variant 123 of , and variant 51 of . shader family 007 shader family 008 Each pair consists of an expected reference image (left image in pair), and a bad variant image rendered by Qualcomm (right image in pair). However, the Qualcomm driver fluctuates nondeterministically between producing this wrong image vs. crashing during compilation. Crash errors Both drivers exhibit a number of crashes on our sample: 28 for Qualcomm vs. 15 for ARM. All are compile-time segmentation faults — Fatal signal 11 (SIGSEGV). The crash data available on Android doesn’t give anything away related to the cause of these crashes. Interestingly, neither driver can handle variant 34 of without crashing. However, as we’ll see in posts, other graphics drivers can handle this variant. shader family 006 future Compile failures The ARM driver exhibits one compiler failure, which occurs for variant 216 of . The issue appears to relate to mishandling of the ternary operator, (?:). Qualcomm shows more compiler failures —nine in total (combining compiler and linker errors), though several of these errors appear to be due to a single underlying issue related to handling of switch statements. shader family 003 Resource issues Our results don’t highlight any Qualcomm resource issues, but do show a resource issue for ARM on variant 122 of , which gives a GL_OUT_OF_MEMORY during rendering. shader family 003 , and try out the ! Check out the full S8 vs. S8 results table GraphicsFuzz Android benchmark web app Next time… … . we’ll see how the Android drivers for NVIDIA’s Shield TV hold up

A tale of two Samsungs: ARM vs. Qualcomm in Android graphics

Uncovering the Vulnerabilities of Qualcomm DSP

Qualcomm Snapdragon 865 will be powering most of the flagship smartphones coming from early 2020. Qualcomm announces its new SoC’s for both high end and mid-range devices, Snapdragon 865 and Snapdragon 765 respectively. Let’s Talk About Snapdragon 865 SoC... Qualcomm Snapdragon 865 is 25% faster in both GPU and CPU performance. This is a usual speed update that Qualcomm gives in its flagship SoC. SD 865 is a 7nm 8 core chipset like the last year but Qualcomm says this is the improved and modified version of the 7nm series. And with these changes, Qualcomm is able to achieve a 25% speed boost and 25% less battery consumption. Snapdragon 865 Upcoming Phones Here is the list of upcoming Phones with Mandatory 5G: OnePlus 8 and 8 Pro Samsung Galaxy S11 Series Google Pixel 5 LG G9 Pocophone F2 Sony Xperia 3 For more compatible phones and official list, we have to wait for . MWC 2020 Performance Qualcomm Update their big cores to Kryo 585 which is based on the ARM Cortex A77 cores. Last year, was based on Keyo 485 cores, which uses ARM Cortex A76 cores. Snapdragon 855 Snapdragon 865 uses the same frequencies as the Last year, One main core at 2.84GHz and the other 3 cores at 2.42 GHz in A77. While the 4 cores of Cortex A55 are made for the smaller background lower power tasks and are clocked at 1.8GHz. Other specification of Snapdragon 865 includes: Hexagon 698 Processor. Adreno 650 GPU (25% Faster) Spectra 480 ISP New Sensing Hub Adreno 650 is 25% faster in rendering and overall GPU performance. Sensing Hub If you have used Pixel 4 before then this is a familiar feature for you. Sensing hub senses the availability of a person near the device. It enables a device to sense the different human gestures and a user can do multiple functions by only using their gestures. Now it’s up to the manufacturers, how they use it. X55 Modem Snapdragon 855 was the first 5G processor to release with the 5G capability. But Qualcomm made a separate modem for 5G called X50 and manufacturers had a choice to add 5G as a separate option. Snapdragon 855 came with a built-in gigabit LTE modem called X24. Last year manufacturers made two models of their flagship devices separately, 5G, and 4G models. This year with the , Qualcomm made 5G as a mandatory feature of every Android flagship. Snapdragon 865 has no built-in modem and all manufacturers have to connect it to a new X55 5G modem to make it work. X55 is the 2nd Gen 5G modem from Qualcomm. Snapdragon 865 5G this year is more improved and compatible with more frequencies. Its has a totally global solution for 5G which includes: NSA Carrier Aggregation SA DSS Sub 6 GHz band Signal Boost Millimeter Waves Envelop Tracking Smart Transit Bigger In-Display Fingerprint Scanner Qualcomm’s new in-display fingerprint scanner is 17 times bigger than its predecessor. It is covering more area on the display and even can scan two fingers simultaneously for improved security. Qualcomm named it 3D sonic max. Improved Camera Qualcomm Snapdragon 865 comes with 2 Gigapixel per second capable Image signal processor. This means you can shoot videos in 8K up to 30 Frames per second. This is the first processor that can shoot 8K at 30FPS. 4K video recording is also improved. Now you can shoot 4K at 64 MP and each frame will be captured at 64 MP. 4K video recording can now support up to 120 FPS and Super Slow-mo at 960 FPS. Qualcomm has merged the two separate files of a portrait image. You won’t see two separate images while taking portraits. They have done it by saving the portrait part of the image separately. Now you will be able to see your images without portrait effect years after taking it. Judder Free Zoom With Qualcomm SD 865, you can now zoom in and out without any Lag. Android phones are known for lagging while zooming in and out. Now this will not be the case with 865. Snapdragon 865 is fast enough to handle the zoom feature without creating any lag. Dolby Colors Dolby colors are the standards of the film industry. Everyone is using it. But filmmakers apply Dolby colors after shooting the footage in raw. With the SD 865, you will be able to see the Dolby applied footage while recording it. Isn’t it amazing? You can see the Dolby applied footage in real-time. Snapdragon Elite Gaming Updates Qualcomm is taking Desktop Level lighting features in mobile gaming with this SoC. Developers can now add high-quality lighting in their gaming and gaming quality will be more improved. They have done it by using forward rendering. Game color Plus: With new Game Color Plus, the user will experience more rich colors in games. Even existing games will look better in devices with SD 865. This feature enhances the colors of the games Updatable GPU drivers: Qualcomm made the drivers of the graphic processing unit separately in Snapdragon 865 chipset. The benefit is that the drivers of GPU can be tweaked and upgrade according to the latest games need. This will give the developers more room to think out of the box and make desktop-level games fro mobile devices. New AI Engine Qualcomm introduced its 5th Gen AI engine in 865. With this real-time translation is much improved in the latest Snapdragon. Your 865 built-in device will be able to do this without any internet connection. This AI engine is capable of doing 15 TOPS which s times the previous Snapdragon Flagship chipset and 3 times faster than other Android SoC competitors. Emojis will also be improved with this chipset. It can now detect very little face expressions with much fewer fails. https: //media.giphy.com/media/3ohhwJ2v6v1bHegoKY/giphy.gif Key Takeaways Qualcomm Snapdragon 865 is the new king in the Android Smartphone market. 5G is a necessary feature as Qualcomm 865 only works with an X55 5G enabled modem. The speed update is conventional but the gaming and UI improvements have made a good jump. Overall it checks all the boxes and you will see 5G devices in the market more rapidly.

Inside the Qualcomm Snapdragon 865: Phones List and more

Can India Secure a Strong Position in the Global Semiconductor Market?

TOKYO, Japan, October 31st, 2024/Chainwire/--Mawari Network a spatial computing DePIN (Decentralized Physical Infrastructure Network), has announced a limited node license sale. Already a pioneer in immersive content, the node sale will make Mawari’s 3D streaming technology more scalable and accessible for content creators and developers. This node sale will help create a global, decentralized spatial computing infrastructure as the market for extended reality devices like the Apple Vision Pro, Meta Orion, and Meta Quest 3 is projected to reach more than 100 million units in the next five years. As demand for immersive experiences grows, Mawari is building a network that can grow with it. Mawari’s infrastructure is optimized for the demanding needs of immersive experiences, positioning Mawari as a key actor in the emerging multibillion-dollar spatial computing market. Called “a sleeping lion” by Samsung Next, Mawari is redefining and building the future of spatial computing. Its solution comprises the Spatial Streaming SDK and the Mawari Network. The SDK, supporting the Unity and Unreal engines, empowers developers to create seamless, immersive experiences. The Mawari Network, working in parallel, leverages a globally distributed network of GPU nodes to ensure low latency and optimal performance. The node license sale's objective is to help scale the network, optimize its global performance, and ensure its resiliency. “This node sale allows node operators to help bring 3D content to the world, and be rewarded for their efforts,” Luis Oscar Ramirez, Mawari’s Founder & CEO said. “The current cloud services didn’t allow us to scale our proprietary spatial streaming technology, we researched ways to scale immersive content and found DePIN was clearly the best option. Now, we can allow our global community of node operators to participate in building out our critical node network alongside our community of developers and content creators.” Mawari has worked with industry leaders, such as Deutsche Telekom, KDDI, Telefonica, Magic Leap, Netflix, Qualcomm, T-Mobile and dozens of other clients. The Solana Foundation recently selected Mawari for a grant, endorsing its roadmap for decentralized spatial computing. Backed by investors including Anfield LTD, Borderless Capital, 1k(x), and Samsung Next, and with veteran DePIN and web3 operator, Sean Carey, a co-founder of Helium, also joining its board, Mawari is the clear category leader. Node Sale Details The node sale is scheduled for Q4 2024, building out the Mawari Network’s ability to perform compute-intensive tasks and efficiently deliver immersive 3D content on a global scale. The Mawari Network will be leveraging specialized GPUs around the world for advanced computation and streaming tasks, but a set of Guardian Nodes are needed to verify their work and the performance of the network overall as it serves multiple simultaneous immersive experiences. The node sale is for licenses to operate these critical Guardian Nodes. More specifically, Guardian Nodes’ responsibilities include assessing critical performance indicators like latency, bandwidth, and content quality to maintain the high standards necessary for immersive, real-time experiences. By monitoring the Mawari network, the community-run Guardian Nodes will make it more performant and more resilient, ensuring it can scale securely as it grows. Details of the node license sale will be announced soon. About Mawari Mawari is setting the standard for decentralized spatial computing and immersive content delivery. The Mawari Network powers real-time streaming of immersive content through a global network of compute nodes. Mawari is optimizing XR for awe, creating unforgettable experiences and revolutionizing how creators engage audiences at scale. Mawari Network built the foundational technologies to power the next generation of interactive media, fully realizing the possibilities of virtual worlds existing seamlessly alongside our physical reality. Its core technology, the Mawari Engine and Spatial Streaming SDK, runs on the Mawari Network — the world’s first and only DePIN (Decentralized Physical Infrastructure Network) that orchestrates storage, bandwidth, and rendering in real-time for spatial computing. Bypassing the rendering limitations of existing mobile devices and content delivery networks, Mawari’s global network of compute nodes has already powered activations from digital fashion shows to concert visualizations. The Mawari Network’s peer-to-peer architecture was purpose-built to scale spatial media streaming requirements with a community-centered ethos. Website X (Twitter) [Discord](https://: https://discord.gg/mawari) Contact Will Haskins will@serotonin.co This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here TOKYO, Japan, October 31st, 2024/Chainwire/--Mawari Network a spatial computing DePIN (Decentralized Physical Infrastructure Network), has announced a limited node license sale. Already a pioneer in immersive content, the node sale will make Mawari’s 3D streaming technology more scalable and accessible for content creators and developers. This node sale will help create a global, decentralized spatial computing infrastructure as the market for extended reality devices like the Apple Vision Pro, Meta Orion, and Meta Quest 3 is projected to reach more than 100 million units in the next five years. As demand for immersive experiences grows, Mawari is building a network that can grow with it. Mawari’s infrastructure is optimized for the demanding needs of immersive experiences, positioning Mawari as a key actor in the emerging multibillion-dollar spatial computing market. Called “a sleeping lion” by Samsung Next , Mawari is redefining and building the future of spatial computing. Its solution comprises the Spatial Streaming SDK and the Mawari Network. The SDK, supporting the Unity and Unreal engines, empowers developers to create seamless, immersive experiences. Samsung Next Samsung Next The Mawari Network, working in parallel, leverages a globally distributed network of GPU nodes to ensure low latency and optimal performance. The node license sale's objective is to help scale the network, optimize its global performance, and ensure its resiliency. “This node sale allows node operators to help bring 3D content to the world, and be rewarded for their efforts,” Luis Oscar Ramirez, Mawari’s Founder & CEO said. “This node sale allows node operators to help bring 3D content to the world, and be rewarded for their efforts,” Luis Oscar Ramirez, Mawari’s Founder & CEO said. “The current cloud services didn’t allow us to scale our proprietary spatial streaming technology, we researched ways to scale immersive content and found DePIN was clearly the best option. Now, we can allow our global community of node operators to participate in building out our critical node network alongside our community of developers and content creators.” “The current cloud services didn’t allow us to scale our proprietary spatial streaming technology, we researched ways to scale immersive content and found DePIN was clearly the best option. Now, we can allow our global community of node operators to participate in building out our critical node network alongside our community of developers and content creators.” Mawari has worked with industry leaders, such as Deutsche Telekom, KDDI, Telefonica, Magic Leap, Netflix, Qualcomm, T-Mobile and dozens of other clients. The Solana Foundation recently selected Mawari for a grant, endorsing its roadmap for decentralized spatial computing. Backed by investors including Anfield LTD, Borderless Capital, 1k(x), and Samsung Next, and with veteran DePIN and web3 operator, Sean Carey, a co-founder of Helium, also joining its board, Mawari is the clear category leader. Node Sale Details The node sale is scheduled for Q4 2024, building out the Mawari Network’s ability to perform compute-intensive tasks and efficiently deliver immersive 3D content on a global scale. The Mawari Network will be leveraging specialized GPUs around the world for advanced computation and streaming tasks, but a set of Guardian Nodes are needed to verify their work and the performance of the network overall as it serves multiple simultaneous immersive experiences. The node sale is for licenses to operate these critical Guardian Nodes. More specifically, Guardian Nodes’ responsibilities include assessing critical performance indicators like latency, bandwidth, and content quality to maintain the high standards necessary for immersive, real-time experiences. By monitoring the Mawari network, the community-run Guardian Nodes will make it more performant and more resilient, ensuring it can scale securely as it grows. Details of the node license sale will be announced soon. About Mawari Mawari is setting the standard for decentralized spatial computing and immersive content delivery. The Mawari Network powers real-time streaming of immersive content through a global network of compute nodes. Mawari is optimizing XR for awe, creating unforgettable experiences and revolutionizing how creators engage audiences at scale. Mawari Mawari Mawari Network built the foundational technologies to power the next generation of interactive media, fully realizing the possibilities of virtual worlds existing seamlessly alongside our physical reality. Its core technology, the Mawari Engine and Spatial Streaming SDK, runs on the Mawari Network — the world’s first and only DePIN (Decentralized Physical Infrastructure Network) that orchestrates storage, bandwidth, and rendering in real-time for spatial computing. Bypassing the rendering limitations of existing mobile devices and content delivery networks, Mawari’s global network of compute nodes has already powered activations from digital fashion shows to concert visualizations. The Mawari Network’s peer-to-peer architecture was purpose-built to scale spatial media streaming requirements with a community-centered ethos. Website Website X (Twitter) X (Twitter) [Discord](https://: https://discord.gg/mawari) Contact Will Haskins will@serotonin.co This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here

Mawari Announces Node Sale To Bring Immersive Content To The World

Mawari kondig node-verkoping aan om meeslepende inhoud na die wêreld te bring

ማዋሪ አስማጭ ይዘትን ወደ አለም ለማምጣት የመስቀለኛ መንገድ ሽያጭን አስታወቀ

أعلنت شركة ماواري عن بيع عقدة لتقديم محتوى غامر للعالم

Mawari oznamuje prodej uzlu, aby světu přinesl pohlcující obsah

Mawari anuncia la venta de Node para llevar contenido inmersivo al mundo

मावारी ने दुनिया भर में इमर्सिव कंटेंट लाने के लिए नोड सेल की घोषणा की

Mawari Umumkan Penjualan Node untuk Hadirkan Konten Imersif ke Seluruh Dunia

Mawari מכריזה על מכירת צומת כדי להביא תוכן סוחף לעולם

Mawari、没入型コンテンツを世界に届けるためノードセールを発表

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Mawari tillkännager Node-försäljning för att ge uppslukande innehåll till världen

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هل أنت جائع يا أخي؟ دعنا نجرب تطبيق توصيل الطعام المفضل لديك

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Authors:
(1) Zhan Ling, UC San Diego and equal contribution;
(2) Yunhao Fang, UC San Diego and equal contribution;
(3) Xuanlin Li, UC San Diego;
(4) Zhiao Huang, UC San Diego;
(5) Mingu Lee, Qualcomm AI Research and Qualcomm AI Research
(6) Roland Memisevic, Qualcomm AI Research;
(7) Hao Su, UC San Diego. Table of Links Abstract and Introduction Related work Motivation and Problem Formulation Deductively Verifiable Chain-of-Thought Reasoning Experiments Limitations Conclusion, Acknowledgements and References A Deductive Verification with Vicuna Models B More Discussion on Improvements of Deductive Verification Accuracy Versus Improvements on Final Answer Correctness C More Details on Answer Extraction D Prompts E More Deductive Verification Examples E More Deductive Verification Examples In this section, we present more deductive verification examples using our Natural Program-based approach on single reasoning steps. In Tab. 18, we demonstrate that the language model (ChatGPT) not only successfully identifies ungrounded information, but also identifies logical errors within the given solutions. In Tab. 19, we illustrate a case where the language model fails to detect ungrounded premise numbers, mistakenly assuming that these numbers can be derived from grounded ones. Lastly, in Tab. 20, we illustrate a case where the language model is sometimes unable to correctly identify grounded numbers. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Zhan Ling, UC San Diego and equal contribution; (2) Yunhao Fang, UC San Diego and equal contribution; (3) Xuanlin Li, UC San Diego; (4) Zhiao Huang, UC San Diego; (5) Mingu Lee, Qualcomm AI Research and Qualcomm AI Research (6) Roland Memisevic, Qualcomm AI Research; (7) Hao Su, UC San Diego. Authors: Authors: (1) Zhan Ling, UC San Diego and equal contribution; (2) Yunhao Fang, UC San Diego and equal contribution; (3) Xuanlin Li, UC San Diego; (4) Zhiao Huang, UC San Diego; (5) Mingu Lee, Qualcomm AI Research and Qualcomm AI Research (6) Roland Memisevic, Qualcomm AI Research; (7) Hao Su, UC San Diego. Table of Links Abstract and Introduction Abstract and Introduction Related work Related work Motivation and Problem Formulation Motivation and Problem Formulation Deductively Verifiable Chain-of-Thought Reasoning Deductively Verifiable Chain-of-Thought Reasoning Experiments Experiments Limitations Limitations Conclusion, Acknowledgements and References Conclusion, Acknowledgements and References A Deductive Verification with Vicuna Models A Deductive Verification with Vicuna Models B More Discussion on Improvements of Deductive Verification Accuracy Versus Improvements on Final Answer Correctness B More Discussion on Improvements of Deductive Verification Accuracy Versus Improvements on Final Answer Correctness C More Details on Answer Extraction C More Details on Answer Extraction D Prompts D Prompts E More Deductive Verification Examples E More Deductive Verification Examples E More Deductive Verification Examples In this section, we present more deductive verification examples using our Natural Program-based approach on single reasoning steps. In Tab. 18, we demonstrate that the language model (ChatGPT) not only successfully identifies ungrounded information, but also identifies logical errors within the given solutions. In Tab. 19, we illustrate a case where the language model fails to detect ungrounded premise numbers, mistakenly assuming that these numbers can be derived from grounded ones. Lastly, in Tab. 20, we illustrate a case where the language model is sometimes unable to correctly identify grounded numbers. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Deductive Verification with Natural Programs: Case Studies

Authors:
(1) Zhan Ling, UC San Diego and equal contribution;
(2) Yunhao Fang, UC San Diego and equal contribution;
(3) Xuanlin Li, UC San Diego;
(4) Zhiao Huang, UC San Diego;
(5) Mingu Lee, Qualcomm AI Research and Qualcomm AI Research
(6) Roland Memisevic, Qualcomm AI Research;
(7) Hao Su, UC San Diego. Table of Links Abstract and Introduction Related work Motivation and Problem Formulation Deductively Verifiable Chain-of-Thought Reasoning Experiments Limitations Conclusion, Acknowledgements and References A Deductive Verification with Vicuna Models B More Discussion on Improvements of Deductive Verification Accuracy Versus Improvements on Final Answer Correctness C More Details on Answer Extraction D Prompts E More Deductive Verification Examples D Prompts D.1 Prompt for Direct Reasoning Chain Verification Without Natural Program Format For the results in Tab. 2 of the main paper, We use “Do you think the above reasoning process is correct? Let’s think step by step.” as the zero-shot prompt to verify an entire reasoning chain at once. We also design a two-shot prompt for reasoning chain verification as shown in Tab. 12, which covers one correct reasoning chain and one incorrect reasoning chain. D.2 Prompts for Reasoning Chain Generation in the Natural Program Format To instruct models to generate reasoning chains in the Natural Program format that facilitates step-by-step deductive verification, we have designed four distinct prompts to address different types of problems. These include: Math word problems, as illustrated in Tab. 13, covering GSM8K, MATH, and AddSub datasets.


Math word problems with multiple-choice options, illustrated in Tab. 14, covering the AQuA dataset.


Date-related problems, illustrated in Tab. 15, covering the Date dataset.


Last Letters problems, illustrated in Tab. 16, covering the Last Letters dataset. D.3 Prompt for Deductive Verification Following Natural Program Format and Step-by-Step Decomposition We have designed a general one-shot prompt for the deductive verification of a single reasoning step on different datasets, as shown in Tab. 17. This prompt serves to instruct language models to generate the deductive validity of each reasoning step as illustrated in Sec. 4.2 and the top-right box of Fig. 1 of the main paper. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Zhan Ling, UC San Diego and equal contribution; (2) Yunhao Fang, UC San Diego and equal contribution; (3) Xuanlin Li, UC San Diego; (4) Zhiao Huang, UC San Diego; (5) Mingu Lee, Qualcomm AI Research and Qualcomm AI Research (6) Roland Memisevic, Qualcomm AI Research; (7) Hao Su, UC San Diego. Authors: Authors: (1) Zhan Ling, UC San Diego and equal contribution; (2) Yunhao Fang, UC San Diego and equal contribution; (3) Xuanlin Li, UC San Diego; (4) Zhiao Huang, UC San Diego; (5) Mingu Lee, Qualcomm AI Research and Qualcomm AI Research (6) Roland Memisevic, Qualcomm AI Research; (7) Hao Su, UC San Diego. Table of Links Abstract and Introduction Abstract and Introduction Related work Related work Motivation and Problem Formulation Motivation and Problem Formulation Deductively Verifiable Chain-of-Thought Reasoning Deductively Verifiable Chain-of-Thought Reasoning Experiments Experiments Limitations Limitations Conclusion, Acknowledgements and References Conclusion, Acknowledgements and References A Deductive Verification with Vicuna Models A Deductive Verification with Vicuna Models B More Discussion on Improvements of Deductive Verification Accuracy Versus Improvements on Final Answer Correctness B More Discussion on Improvements of Deductive Verification Accuracy Versus Improvements on Final Answer Correctness C More Details on Answer Extraction C More Details on Answer Extraction D Prompts D Prompts E More Deductive Verification Examples E More Deductive Verification Examples D Prompts D.1 Prompt for Direct Reasoning Chain Verification Without Natural Program Format For the results in Tab. 2 of the main paper, We use “Do you think the above reasoning process is correct? Let’s think step by step.” as the zero-shot prompt to verify an entire reasoning chain at once. We also design a two-shot prompt for reasoning chain verification as shown in Tab. 12, which covers one correct reasoning chain and one incorrect reasoning chain. D.2 Prompts for Reasoning Chain Generation in the Natural Program Format To instruct models to generate reasoning chains in the Natural Program format that facilitates step-by-step deductive verification, we have designed four distinct prompts to address different types of problems. These include: Math word problems, as illustrated in Tab. 13, covering GSM8K, MATH, and AddSub datasets. Math word problems with multiple-choice options, illustrated in Tab. 14, covering the AQuA dataset. Date-related problems, illustrated in Tab. 15, covering the Date dataset. Last Letters problems, illustrated in Tab. 16, covering the Last Letters dataset. Math word problems, as illustrated in Tab. 13, covering GSM8K, MATH, and AddSub datasets. Math word problems, as illustrated in Tab. 13, covering GSM8K, MATH, and AddSub datasets. Math word problems with multiple-choice options, illustrated in Tab. 14, covering the AQuA dataset. Math word problems with multiple-choice options, illustrated in Tab. 14, covering the AQuA dataset. Date-related problems, illustrated in Tab. 15, covering the Date dataset. Date-related problems, illustrated in Tab. 15, covering the Date dataset. Last Letters problems, illustrated in Tab. 16, covering the Last Letters dataset. Last Letters problems, illustrated in Tab. 16, covering the Last Letters dataset. D.3 Prompt for Deductive Verification Following Natural Program Format and Step-by-Step Decomposition We have designed a general one-shot prompt for the deductive verification of a single reasoning step on different datasets, as shown in Tab. 17. This prompt serves to instruct language models to generate the deductive validity of each reasoning step as illustrated in Sec. 4.2 and the top-right box of Fig. 1 of the main paper. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Essential Prompts for Reasoning Chain Verification and Natural Program Generation

Authors:
(1) Zhan Ling, UC San Diego and equal contribution;
(2) Yunhao Fang, UC San Diego and equal contribution;
(3) Xuanlin Li, UC San Diego;
(4) Zhiao Huang, UC San Diego;
(5) Mingu Lee, Qualcomm AI Research and Qualcomm AI Research
(6) Roland Memisevic, Qualcomm AI Research;
(7) Hao Su, UC San Diego. Table of Links Abstract and Introduction Related work Motivation and Problem Formulation Deductively Verifiable Chain-of-Thought Reasoning Experiments Limitations Conclusion, Acknowledgements and References A Deductive Verification with Vicuna Models B More Discussion on Improvements of Deductive Verification Accuracy Versus Improvements on Final Answer Correctness C More Details on Answer Extraction D Prompts E More Deductive Verification Examples C More Details on Answer Extraction In this section, we describe our process to extract the final answer from language models’ responses. The process begins by selecting the last three non-empty lines. Then, these lines are processed through the following pipeline: Firstly, we use a list of regular expressions to identify "No-Answer" patterns within the text, such as "we cannot answer (this|the) question". This process helps us ascertain whether the model can provide a conclusive answer. If any such patterns appear in the text, we mark "No answer!" as the final answer. However, if we don’t detect these patterns, we proceed to the next steps for extracting the final answer.


Secondly, if any "Answer-Split" patterns are found in the text, we divide the text into several blocks using the identified pattern. The last block of text is then utilized for extracting the answer.


Lastly, we use regular expressions, as outlined in Tab. 11, to scan the remaining text for possible final answers. If multiple matches are found for the pattern, we select the first match as the final answer. If no pattern matches are found in the remaining text, we default the final response to "No answer!". “No-Answer” Patterns: "we cannot provide an answer to this question with (this|the) given information", "we cannot answer (this|the) question", "we cannot determine", "we can’t determine", "we do not have enough information to answer (this|the) question", "we do not have enough information to provide a definitive answer to (this|the) question", "the answer(.*?)is unknown", "answer is not listed among the answer choices". “Answer-Split” Patterns: "answer is", "final answer:", "answer to the question is", "answer to this question is", "concatenated letters are", "concatenate the letters -", "The answer of ". This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Zhan Ling, UC San Diego and equal contribution; (2) Yunhao Fang, UC San Diego and equal contribution; (3) Xuanlin Li, UC San Diego; (4) Zhiao Huang, UC San Diego; (5) Mingu Lee, Qualcomm AI Research and Qualcomm AI Research (6) Roland Memisevic, Qualcomm AI Research; (7) Hao Su, UC San Diego. Authors: Authors: (1) Zhan Ling, UC San Diego and equal contribution; (2) Yunhao Fang, UC San Diego and equal contribution; (3) Xuanlin Li, UC San Diego; (4) Zhiao Huang, UC San Diego; (5) Mingu Lee, Qualcomm AI Research and Qualcomm AI Research (6) Roland Memisevic, Qualcomm AI Research; (7) Hao Su, UC San Diego. Table of Links Abstract and Introduction Abstract and Introduction Related work Related work Motivation and Problem Formulation Motivation and Problem Formulation Deductively Verifiable Chain-of-Thought Reasoning Deductively Verifiable Chain-of-Thought Reasoning Experiments Experiments Limitations Limitations Conclusion, Acknowledgements and References Conclusion, Acknowledgements and References A Deductive Verification with Vicuna Models A Deductive Verification with Vicuna Models B More Discussion on Improvements of Deductive Verification Accuracy Versus Improvements on Final Answer Correctness B More Discussion on Improvements of Deductive Verification Accuracy Versus Improvements on Final Answer Correctness C More Details on Answer Extraction C More Details on Answer Extraction D Prompts D Prompts E More Deductive Verification Examples E More Deductive Verification Examples C More Details on Answer Extraction In this section, we describe our process to extract the final answer from language models’ responses. The process begins by selecting the last three non-empty lines. Then, these lines are processed through the following pipeline: Firstly, we use a list of regular expressions to identify "No-Answer" patterns within the text, such as "we cannot answer (this|the) question". This process helps us ascertain whether the model can provide a conclusive answer. If any such patterns appear in the text, we mark "No answer!" as the final answer. However, if we don’t detect these patterns, we proceed to the next steps for extracting the final answer. Secondly, if any "Answer-Split" patterns are found in the text, we divide the text into several blocks using the identified pattern. The last block of text is then utilized for extracting the answer. Lastly, we use regular expressions, as outlined in Tab. 11, to scan the remaining text for possible final answers. If multiple matches are found for the pattern, we select the first match as the final answer. If no pattern matches are found in the remaining text, we default the final response to "No answer!". Firstly, we use a list of regular expressions to identify "No-Answer" patterns within the text, such as "we cannot answer (this|the) question". This process helps us ascertain whether the model can provide a conclusive answer. If any such patterns appear in the text, we mark "No answer!" as the final answer. However, if we don’t detect these patterns, we proceed to the next steps for extracting the final answer. Firstly, we use a list of regular expressions to identify "No-Answer" patterns within the text, such as "we cannot answer (this|the) question". This process helps us ascertain whether the model can provide a conclusive answer. If any such patterns appear in the text, we mark "No answer!" as the final answer. However, if we don’t detect these patterns, we proceed to the next steps for extracting the final answer. Secondly, if any "Answer-Split" patterns are found in the text, we divide the text into several blocks using the identified pattern. The last block of text is then utilized for extracting the answer. Secondly, if any "Answer-Split" patterns are found in the text, we divide the text into several blocks using the identified pattern. The last block of text is then utilized for extracting the answer. Lastly, we use regular expressions, as outlined in Tab. 11, to scan the remaining text for possible final answers. If multiple matches are found for the pattern, we select the first match as the final answer. If no pattern matches are found in the remaining text, we default the final response to "No answer!". Lastly, we use regular expressions, as outlined in Tab. 11, to scan the remaining text for possible final answers. If multiple matches are found for the pattern, we select the first match as the final answer. If no pattern matches are found in the remaining text, we default the final response to "No answer!". “No-Answer” Patterns : "we cannot provide an answer to this question with (this|the) given information", "we cannot answer (this|the) question", "we cannot determine", "we can’t determine", "we do not have enough information to answer (this|the) question", "we do not have enough information to provide a definitive answer to (this|the) question", "the answer(.*?)is unknown", "answer is not listed among the answer choices". “No-Answer” Patterns “Answer-Split” Patterns: "answer is", "final answer:", "answer to the question is", "answer to this question is", "concatenated letters are", "concatenate the letters -", "The answer of ". “Answer-Split” Patterns: This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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