text
stringlengths 301
426
| source
stringclasses 3
values | __index_level_0__
int64 0
404k
|
|---|---|---|
Deep Learning, Natural Language Process, Machine Learning, Markov Chains, Nlp Tutorial.
Face Data Science Simplified: What is language modeling for NLP? Crack the top 40 machine learning interview questions Start a discussion Why do you want to advance with NLP and machine learning? Was this article helpful? Let us know in the comments below! | medium | 1,446 |
LG, Africa, Partnerships, Nigeria, Gadgets.
PRESENTED BY LG AFRICA The company has began offering free laundry services to residents of Benin City, Nigeria. LG Electronics, one of the global leaders in consumer electronics, has opened its fourth free laundry centre in Nigeria, with capacity to wash and dry about 500 set of clothes, as part | medium | 1,447 |
LG, Africa, Partnerships, Nigeria, Gadgets.
of its effort to give back to the community with a state of art hygienic free laundry centre. The free laundry facility, which is situated at the heart of Benin City at 6, Country Home Motel Road, Off Sapele road, Benin business district, is a follow up to similar wash centre established by the | medium | 1,448 |
LG, Africa, Partnerships, Nigeria, Gadgets.
company in Lagos, Port-Harcourt and Kano initiated under its corporate social responsibility (CSR). Needless to mention that the laundry centre in Ogba, Lagos has been upgraded to the most recently built commercial laundry machines to cater for more families. Speaking during the launch, after | medium | 1,449 |
LG, Africa, Partnerships, Nigeria, Gadgets.
commissioning the centre in Benin City, Mr. Brian Kang, General Manager, Home Appliances Division, LG Electronics West Africa operations, said that the initiative was designed in order to improve the living conditions as well as support the daily washing needs of people in Nigeria of which the | medium | 1,450 |
LG, Africa, Partnerships, Nigeria, Gadgets.
project started about three years ago in Lagos. “LG Electronics has over the years continued to receive commendation from Nigerian consumers for having their interest at heart by providing cutting edge technological products. The three operational centres have been able to cater for over 24,000 | medium | 1,451 |
LG, Africa, Partnerships, Nigeria, Gadgets.
families and washed over 300,000 clothes. This is significant for us as a socially responsive company. The wash centre that we are opening here in Benin City today, has the capacity to wash 500 sets of clothes per day for over 40 persons, and this we believe would go a long way in assisting the | medium | 1,452 |
LG, Africa, Partnerships, Nigeria, Gadgets.
people of the state in meeting their washing needs,” stated Mr. Kang. He further disclosed that the washing centers are operating free of cost to end users, while calling on the residents of the state to take full. advantage of the support being rendered to them by the company. In addition, Mr. | medium | 1,453 |
LG, Africa, Partnerships, Nigeria, Gadgets.
Hari Elluru, Head of Marketing, LG Electronics West Africa Operations expressed the determination of the company to lead sustainability through inclusive growth in Nigeria whilst ensuring that the business community enjoy the best advanced technologically built electronic products. “For us, we | medium | 1,454 |
LG, Africa, Partnerships, Nigeria, Gadgets.
believe that ‘Life`s Good’ when we share with others. We have remained competitive whilst improving sustainability, we have enabled investment and innovation required to deploy new technologies safely and responsibly develop progressive products. We understand that doing well is good business and | medium | 1,455 |
LG, Africa, Partnerships, Nigeria, Gadgets.
so being able to launch our fourth Free Wash Centre in Benin City is a social investment we are happy to deeply go into and to stay in for as long as possible. This is what I truly call giving back to the society because the real beneficiaries of this project are directly the people, whom I believe | medium | 1,456 |
LG, Africa, Partnerships, Nigeria, Gadgets.
will utilize it very well to make life better for them. LG’s philosophy revolves around people, sincerity, and sticking to fundamentals. It is to understand our customers and to offer optimum solutions and new experiences through ceaseless innovation, thus helping our customers better their lives,” | medium | 1,457 |
LG, Africa, Partnerships, Nigeria, Gadgets.
Mr. Elluru stated. Also present at the commissioning was the representative of Fouani Nigeria Limited, Benin City Branch Manager, Mr. Hadi Hobballah who said: “We are so happy to witness this state-of-the-art free wash centre by our partner, LG Electronics. Our customers are extremely appreciative | medium | 1,458 |
LG, Africa, Partnerships, Nigeria, Gadgets.
of this kind gesture and we believe this will help them to further keep clean in a way that promotes hygienic living.” LG Electronics is focused on strategic ways to touch lives and make life better for the people across the globe. The company says it would continue to replicate this gesture across | medium | 1,459 |
Numerical Methods, Advection, Leapfrog, Pde, Fdtd.
Solving the 1D Advection Equation — Numerical Discretization — Leapfrog Approach The advection equation is a simple first-order, partial differential equation (PDE) in which the unknown u satisfies: where t is time, x is space and v is the advection speed. The advection equation requires an initial | medium | 1,461 |
Numerical Methods, Advection, Leapfrog, Pde, Fdtd.
condition: and a boundary condition In the above equations, (xmin, xmax) is the domain in which the equation should be solved, while (tstart, tend) is the time interval of interest. From the numerical point of view, the advection equation can be efficiently solved by a leapfrog approach. Time and | medium | 1,462 |
Numerical Methods, Advection, Leapfrog, Pde, Fdtd.
space discretizations are introduced as: where x0=xmin, xN=xmax, t0=tstart, tM=tend. In this way, we are not searching for the values of u for x and t in continuous domains, but our unknowns become the samples: Under the above discretization, the initial condition is given by while the boundary | medium | 1,463 |
Numerical Methods, Advection, Leapfrog, Pde, Fdtd.
condition is given by The time and spatial derivatives are approximated by central differences (see Numerical approximation of derivatives) as which leads to the following leapfrog update rule where is the so-called Courant number. As it can be seen from the update rule, in order to start the | medium | 1,464 |
Numerical Methods, Advection, Leapfrog, Pde, Fdtd.
iterations, besides the initial condition, we also need To generate an estimate of the solution at time step #1, we can use the Forward Time Central Space (Numerical approximation of derivatives) scheme which, for m=0, leads to Furthermore, from the update rule, the element with index N would | medium | 1,465 |
Numerical Methods, Advection, Leapfrog, Pde, Fdtd.
require an element with index N+1 which would be outside the solution domain. For this reason, the Mur boundary condition is employed, i.e.: where The Courant number must meet the following condition (Courant-Friedrichs-Lewy, or CFL, condition): to guarantee numerical stability of the algorithm. | medium | 1,466 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
Sports have always been a driving force in popular culture, captivating audiences around the world with thrilling competitions, iconic players, and a sense of community and camaraderie among fans. In recent years, however, a new technology has begun to infiltrate the sports landscape in a big way — | medium | 1,467 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
cryptocurrency. Cryptocurrencies, and the blockchain technology that underpins them, are poised to transform nearly every aspect of the sports industry, from fan engagement and ticket sales to athlete compensation and the way sports organizations operate behind the scenes. By providing a secure, | medium | 1,468 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
decentralized, and transparent platform for financial transactions and data management, cryptocurrencies are empowering fans, teams, leagues, and athletes in unprecedented ways. The Rise of Fan Tokens One of the most prominent ways that cryptocurrency is impacting the sports world is through the | medium | 1,469 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
emergence of fan tokens. These digital assets, which are built on blockchain technology, allow teams and leagues to engage with their fans in new and innovative ways. Fan tokens, which are typically launched and managed through partnerships with dedicated cryptocurrency platforms like Chiliz, give | medium | 1,470 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
fans a sense of ownership and involvement in their favorite teams. By holding these tokens, fans can access exclusive rewards, experiences, and decision-making power within the team’s ecosystem. For example, Socios.com, a leading fan engagement platform powered by the Chiliz blockchain, has | medium | 1,471 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
partnered with dozens of top-tier sports organizations around the world, including soccer giants like Paris Saint-Germain, Juventus, and Barcelona, as well as esports teams, basketball franchises, and more. Fans who purchase these teams’ fan tokens can then use them to vote on certain club | medium | 1,472 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
decisions, such as jersey designs, player transfers, and even in-game tactics. This level of fan involvement and influence is unprecedented in the sports industry, and it’s driving a new wave of fan engagement and loyalty. Fans are no longer just passive spectators; they’re actively shaping the | medium | 1,473 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
future of their favorite teams and feeling a deeper connection to the organizations they support. Beyond voting rights, fan tokens also open up new revenue streams for teams and leagues. Fans can purchase these digital assets, trade them on secondary markets, and even earn them through various fan | medium | 1,474 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
engagement activities. This creates a virtuous cycle where teams incentivize fan participation, and fans are rewarded for their loyalty and involvement. The success of fan tokens has been nothing short of remarkable. In 2021, Socios.com reported that its platform had already amassed more than 1.2 | medium | 1,475 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
million registered users, with fan token sales exceeding $150 million. And this is just the beginning — as more sports organizations recognize the power of fan tokens, we can expect to see even greater adoption and innovation in this space. Blockchain-Based Ticketing and Fan Engagement | medium | 1,476 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
Cryptocurrencies and blockchain technology are also transforming the ticketing and fan engagement landscape in sports. By leveraging the secure, transparent, and tamper-resistant nature of blockchain, teams and leagues can offer a range of new experiences and benefits to their fans. One of the most | medium | 1,477 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
immediate applications of blockchain in sports ticketing is the ability to combat ticket scalping and fraud. Traditional paper or digital tickets are vulnerable to counterfeiting and resale at inflated prices, which can frustrate fans and deprive teams of potential revenue. Blockchain-based | medium | 1,478 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
ticketing, on the other hand, provides a secure, decentralized platform for ticket distribution and management. When fans purchase tickets through a blockchain-powered platform, the tickets are stored as unique, non-fungible tokens (NFTs) on the blockchain. This ensures that each ticket is | medium | 1,479 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
verifiable, tamper-proof, and easily transferable, while also allowing teams to maintain control over the secondary market and prevent unauthorized resale. Moreover, blockchain-based ticketing systems can offer a range of additional benefits to fans, such as exclusive digital collectibles, rewards | medium | 1,480 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
programs, and seamless access to stadiums and venues. By integrating these features into the ticketing experience, teams and leagues can drive deeper fan engagement and loyalty. One notable example of blockchain-based ticketing in sports is the partnership between the NFL’s San Francisco 49ers and | medium | 1,481 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
the cryptocurrency platform Voyager Digital. In 2021, the 49ers launched a blockchain-powered ticketing system that allows fans to purchase tickets as NFTs, which can then be easily traded or resold on the secondary market. Similarly, the NBA’s Sacramento Kings have been at the forefront of | medium | 1,482 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
blockchain adoption in sports, launching their own “Sacramento Kings Token” and using blockchain technology to power their ticketing and fan engagement initiatives. By harnessing the power of cryptocurrency, the Kings have been able to create a more secure, transparent, and rewarding experience for | medium | 1,483 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
their fans. As more sports organizations recognize the benefits of blockchain-based ticketing, we can expect to see this technology become increasingly widespread in the industry. It’s a powerful tool for enhancing fan experiences, combating fraud, and unlocking new revenue streams for teams and | medium | 1,484 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
leagues. Cryptocurrency and Athlete Compensation Cryptocurrencies are also poised to revolutionize the way athletes are compensated and paid. In an industry that has traditionally been dominated by complex contract negotiations, opaque financial arrangements, and significant overhead costs, | medium | 1,485 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
blockchain technology offers a more transparent, efficient, and equitable solution. One of the key advantages of using cryptocurrencies for athlete payments is the ability to bypass traditional financial intermediaries, such as banks and payment processors, which can often take a significant cut of | medium | 1,486 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
the funds. By leveraging blockchain-based payment systems, teams and leagues can ensure that a greater percentage of an athlete’s compensation reaches their intended recipient, without the added fees and delays associated with traditional banking. Moreover, cryptocurrencies offer a level of | medium | 1,487 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
transparency and traceability that is unparalleled in the sports industry. Every transaction can be recorded on the blockchain, providing a tamper-proof, public ledger of all payments made to athletes. This can help to build trust, reduce the risk of financial impropriety, and ensure that athletes | medium | 1,488 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
are being fairly compensated for their contributions to their respective teams and leagues. Beyond just facilitating payments, cryptocurrencies also open up new avenues for athlete compensation and revenue generation. For example, some athletes have started to accept a portion of their salaries in | medium | 1,489 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
cryptocurrencies, allowing them to participate in the potential upside of these digital assets. Additionally, athletes can explore opportunities to monetize their personal brands and digital assets, such as non-fungible tokens (NFTs) featuring their images, highlights, or other collectibles. One | medium | 1,490 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
notable example of a sports organization embracing cryptocurrency for athlete compensation is the NFL’s Oakland Raiders. In 2021, the team announced that they would be offering their players the option to receive a portion of their salaries in Bitcoin, making them one of the first professional | medium | 1,491 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
sports franchises to do so. Similarly, the NBA’s Dapper Labs, the company behind the popular NBA Top Shot platform, has been working with individual players to create and sell NBA-themed NFTs, providing athletes with a new revenue stream and a way to engage with their fans in innovative ways. As | medium | 1,492 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
more athletes and teams recognize the benefits of cryptocurrency-based compensation and revenue generation, we can expect to see this trend continue to gain momentum in the sports industry. It’s a powerful tool for driving greater transparency, efficiency, and innovation in the way athletes are | medium | 1,493 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
paid and rewarded for their contributions to the games we love. The Future of Cryptocurrency in Sports As the integration of cryptocurrency and blockchain technology in the sports industry continues to evolve, we can expect to see even more innovative and transformative use cases emerge in the | medium | 1,494 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
years to come. One area that holds tremendous promise is the use of decentralized finance (DeFi) protocols to create new investment opportunities and revenue streams for sports organizations and their fans. Imagine a future where fans can stake their fan tokens to earn rewards, or where teams can | medium | 1,495 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
leverage DeFi platforms to raise capital for stadium renovations or player acquisitions. Another exciting frontier is the integration of non-fungible tokens (NFTs) with sports-related content and experiences. Beyond just digital collectibles, NFTs could be used to represent unique, one-of-a-kind | medium | 1,496 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
experiences, such as courtside seats, behind-the-scenes access, or even the opportunity to interact with players and coaches. As the metaverse and virtual reality technologies continue to advance, we may also see the rise of new, crypto-powered sports experiences. Fans could attend virtual games, | medium | 1,497 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
purchase digital merchandise, and even participate in virtual sports competitions, all powered by blockchain-based platforms and currencies. The potential for cryptocurrency to revolutionize the sports industry is vast and largely untapped. By providing a secure, transparent, and decentralized | medium | 1,498 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
platform for financial transactions, data management, and fan engagement, cryptocurrencies are empowering athletes, teams, leagues, and fans in ways that were previously unimaginable. As the adoption of cryptocurrency continues to grow, both within the sports industry and the broader global | medium | 1,499 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
economy, we can expect to see even more innovative and disruptive applications of this transformative technology. The future of sports is undoubtedly tied to the rise of cryptocurrency, and the organizations and individuals who embrace this new frontier will be the ones who shape the industry for | medium | 1,500 |
Sports, Web3, Cryptocurrency, Blockchain, Technology.
https://www.coindesk.com/business/2021/10/22/socioscom-the-rise-of-fan-tokens-in-the-sports-industry/ https://www.sporttechie.com/cryptocurrency-and-the-future-of-athlete-compensation https://www.sportbusiness.com/2021/06/the-potential-of-nfts-in-the-sports-industry/ | medium | 1,502 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
Photo by Michaela on Unsplash The architecture of choice to facilitate further software advances in AI John Hennessy and David Patterson gave their Turing lecture A New Golden Age for Computer Architecture on June 4, 2018, as the recipients of the 2017 Turing Award, the equivalent of the Nobel | medium | 1,503 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
Prize for Computer Science. The three key insights of the lecture are: Software advances can inspire architecture innovation. Elevating the hardware/software interface creates opportunities for architecture innovation. The marketplace ultimately settles the architecture debates. I want to complete | medium | 1,504 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
the loop by amending the three key insights with a fourth one: The winning architecture facilitates the subsequent software advances. Since the Hennessy/Patterson lecture, the marketplace has arguably accomplished insight #3 in AI and settled on the Graphics Processing Unit (GPU) as the winning | medium | 1,505 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
architecture that has facilitated the AI revolution. In this article, I explore how the AI revolution is inspiring architecture innovations and re-inventing the GPU. I hope to answer a significant question of my own: Will the GPU star in a new golden age for computer architecture? Domain-Specific | medium | 1,506 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
Architecture Henessy and Patterson proposed Domain-Specific Architecture (DSA) to innovate computer architecture and strive toward a new golden age. As the name suggests, the GPU is a DSA for 3D Graphics. It aims to render photo-realistic images of the 3D virtual world; however, almost all AI | medium | 1,507 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
researchers use the GPU to explore ideas beyond 3D Graphics, making breakthroughs in AI “software,” a.k.a. Neural Network architectures. While still indispensable in 3D, the GPU has become the “CPU” of AI since it facilitates software innovations in AI. GPU architects have been making available the | medium | 1,508 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
GPU’s computing resources for non-3D, in addition to 3D uses. We dub this design philosophy General-Purpose GPU (GPGPU). Nowadays, we see a proliferation of AI DSAs instead of GPGPU, attempting to replace the GPU with better performance. Even the GPU itself is struggling between its dual | medium | 1,509 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
personalities: AI DSA and 3D DSA. The reason is that AI DSA requires accelerating tensor operations, which are abundant in AI but not in 3D. At the same time, 3D fixed-function hardware sounds unnecessary for AI. Thus, the primary architecture debate seems to ask Will the GPU keep its throne as the | medium | 1,510 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
“CPU” of AI? Will the GPU diverge into two DSAs, one for AI and the other for 3D? My prediction is the following: The GPU hardware/software interface will keep the GPU the “CPU” for AI. AI-based Rendering will make tensor acceleration a mainstay in the GPU. Digital Twin, in which the virtual and | medium | 1,511 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
the real worlds mirror each other, will preside over the marketplace, at last settling the architecture debate. GPU Hardware/Software Interface We can attribute the GPU’s dominance in 3D and runaway success in AI to its hardware/software interface, which GPU and 3D Graphics software architects | medium | 1,512 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
endeavor to embrace. This interface is the key to resolving the following paradox. While the GPU community continues to make the GPU more general-purpose, the rest of the industry has switched to more specialized hardware to work around the demise of Moore’s Law. The GPU Pipeline (Image by Author) | medium | 1,513 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
Two-Tier Programmability A GPU is conceptually a long linear pipeline of processing stages. Different types of work items are processed as they flow through the pipeline. In the early days, each processing stage was a fixed-function block. The only control programmers had over the GPU was to tweak | medium | 1,514 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
the parameters of each block. These days, the GPU hardware/software interface gives programmers the freedom to do as they please with each work item, be it a vertex or a pixel. There is no need to address the loop head in each vertex or pixel loop, as GPU architects implement it in a fixed | medium | 1,515 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
function. This architectural choice leaves the programmers with the responsibility to concern themselves with the loop body, or “shader,” which is often named after the type of work item, such as “vertex shader” for processing vertices and “pixel shader” for handling pixels. How do modern games | medium | 1,516 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
produce stunning pictures with such a linear pipeline? In addition to controlling different types of shaders in one pass through the pipeline, programmers can progressively go through the pipeline multiple times to produce intermediate images that ultimately yield the final image seen on the | medium | 1,517 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
screen. Programmers effectively create a computation graph, describing the relationships among the intermediate images. Each node in the graph represents one pass through the GPU pipeline. A Centralized Pool of General-Purpose Computing Resources A centralized pool of general-purpose computing | medium | 1,518 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
resources is shared among the processing stages and does the heavy lifting. The initial motivation for such a scheme was load balancing; a processing stage may have drastically varying workloads in different usage scenarios. The computing resources, referred to as the Shader Cores, have become more | medium | 1,519 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
general-purpose to achieve flexibility and product differentiation. GPU architects opportunistically made the centralized shader pool available to non-3D applications as GPGPU. This design scheme enabled the GPU to achieve breakthroughs in running AI tasks even as a part-time job. Balanced | medium | 1,520 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
Specialization GPU architects regularly “accelerate” or “domain-specify” the shader pool by adding co-processing units without altering the hardware/software interface. The Texture unit is such a co-processing unit, with which texels in texture maps are fetched and filtered on their way to the | medium | 1,521 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
shader pool. The Special Function Unit (SFU) is another co-processing unit that performs transcendental math functions, such as logarithms, inverse square roots, etc. Although having multiple functions sounds similar to the superscalar design in a CPU, there is one significant difference: GPU | medium | 1,522 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
architects apportion the throughput of a co-processing unit according to how often an “average” shader program uses it. For example, we can give Texture units one-eighth of the throughput of the shader pool, assuming Texture operations appear in benchmarks or games on average one-eighth of the | medium | 1,523 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
time. When a co-processing unit is busy, the GPU switches tasks to keep itself occupied. Tensor Acceleration for 3D In my introduction, I pointed out that the GPU struggled to adopt tensor acceleration in 3D. Let’s see how this trend might reverse if we change how a GPU renders a typical game | medium | 1,524 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
frame. The GPU first generates and stores all information necessary to shade a pixel in G-buffer for each pixel. From G-buffers, we calculate how to light a pixel, followed by several processing steps, including Remove jagged edges (anti-aliasing (AA)) Upscale a low-resolution image to a higher one | medium | 1,525 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
(super-resolution (SR)) Add to the whole frame specific visual effects, such as Ambient Occlusion, Motion Blur, Bloom Filter, or Depth-of-Field. We call this rendering scheme Deferred Shading since shading a pixel is “deferred” until every pixel gets the information it needs. We refer to the | medium | 1,526 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
processing steps following lighting as Post-Processing. Today, Post-Processing consumes about 90% of the rendering time, meaning that a GPU spends its screen time predominantly in 2D instead of 3D! NVIDIA has demonstrated AI-based DLSS 2.0 for AA and SR, which claims to produce better-looking | medium | 1,527 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
images than those rendered natively without DLSS 2.0. In addition, NVIDIA also offers AI-based Monte-Carlo de-noising for Ray Tracing, with which we can use few rays to achieve the quality that’s otherwise only possible with many more rays. Moreover, AI inspires a new class of solutions to many | medium | 1,528 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
other types of Post-Processing, such as NNAO for Ambient Occlusion and DeepLens for Depth-of-Field. If AI-based Post-Processing becomes mainstream, tensor acceleration will become a mainstay in the 3D side of the GPU’s personality. The GPU’s divergence into 3D DSA and AI DSA will become less | medium | 1,529 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
likely. 3D/AI Convergence To settle the architecture debate, we will want to address the last piece of the puzzle: should we eventually remove the fixed-function hardware in 3D rendering, especially for AI? Note that through GPGPU, the GPU can do the 3D rendering as pure “software” without using | medium | 1,530 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
any fixed-function hardware. In a strict sense, given the scene parameters, 3D rendering simulates how photons are transported from light sources through space to interact with objects in the 3D virtual world. Conventional 3D rendering by the GPU is a very crude approximation of this process. Thus, | medium | 1,531 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
Microsoft said that “[Conventional rasterization-based] 3D Graphics is a lie” in an announcement to promote Ray Tracing as “the full 3D effects of tomorrow.” However, a 3D rendering purist might still dismiss Ray Tracing, in which we achieve 3D rendering by tracing the rays of light backward from | medium | 1,532 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
the pixels into the 3D virtual world, as also not truthful. Both approaches are approximations to simulation-based 3D rendering. In either case, we decouple modeling of the 3D virtual world, or content creation, from rendering. In the first case, modeling the 3D virtual world requires engineers and | medium | 1,533 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
artists a vast amount of laborious and creative work to describe every object and its physical property regarding how it interacts with lights. In the second case, regarding rendering, total truthfulness is impossible since we need to drastically simplify 3D rendering to meet different performance | medium | 1,534 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
targets within resource budgets. In contrast to finding a solution with the best-known scientific knowledge and mathematical theories for a given problem, the AI approach is about “learning” a computational model, or a Neural Network, from data. We adjust the network parameters iteratively by trial | medium | 1,535 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
and error. We run the network forward through previous parameter estimates and measure mismatch or “loss.” We then adjust the parameters to reduce the loss according to its gradient, effectively navigating the loss landscape in the opposite direction of the gradient. This mechanism, referred to as | medium | 1,536 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
backpropagation, requires all computations along the forward path to be differentiable to participate in calculating the gradient. Neural Rendering is an emerging AI research field that studies 3D rendering using the approach described above. Below is my mindmap to keep track of progress in Neural | medium | 1,537 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
Rendering: Image by Author This model of the 3D virtual world is represented implicitly as Neural Network parameters (see NeRF, GRAF, GIRAFFE), which we infer from comparing the real-world images with the ones we render from the virtual world. Then we backpropagate the gradient of the comparison to | medium | 1,538 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
adjust the Neural Network parameters. Optionally, we can learn explicit 3D meshes from data (see Deep Marching Cube, GAN2Shape). Effectively, modeling the 3D virtual world is the same thing as learning the Neural Network parameters. This process requires us to include a 3D rendering pipeline in the | medium | 1,539 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
forward path and integrate modeling and rendering of the 3D virtual world in tight loops. Through iterations of rendering and testing against real-world images, we obtain the desired models and scene parameters that we can use to render new views of the virtual world. Within this framework, we can | medium | 1,540 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
choose not to adjust the whole of each parameter, for example, keeping the shape of an object intact but estimating its location (see iNeRF). This way, we effectively try to recognize and locate the object in question instead of modeling it. There is no longer a difference between modeling and | medium | 1,541 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
recognition tasks. Instead, It is a matter of which scene parameters we want to “learn” or “estimate.” Conclusion Thus, under the AI problem-solving paradigm, 3D rendering is not only about producing photo-realistic images of the 3D virtual world but also for building the virtual world from the | medium | 1,542 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
real world. Furthermore, the new framework redefines 3D and AI in the following ways: 3D rendering becomes an essential operation in the training loop of AI Training, or “gradient descent,” which used to happen only to train Neural Networks in the cloud, is now part of inference. Photo-realism is | medium | 1,543 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
as much about looking great as maintaining the correspondence between the real and the virtual worlds. Digital Twin will demand bringing the massive and ever-changing real world to its under-developed twin and constantly maintaining the correspondence between the twins. The virtual objects acquired | medium | 1,544 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
through Neural Rendering need to co-exist with classically built ones. Hence, I believe Neural Rendering and conventional rendering will converge on the GPU, leveraging its mature and performant 3D pipeline. The demands of Digital Twins will fall on the shoulders of future GPUs. Work needs to be | medium | 1,545 |
AI, 3d, Computer Architecture, Gpu, Digital Twin.
done on the GPU side to become “differentiable” in order to participate in the AI training loop’s gradient calculation. Suppose the GPU becomes natively differentiable and tensor-accelerated in response to the AI advances in 3D, I foresee the dual personalities of the GPU becoming one. Then, the | medium | 1,546 |
Machine Learning, Deep Learning, Naturallanguageprocessing.
If you’re a keen Natural Language Processing practitioner, and have some deep learning background, Google BERT—while no longer cutting edge— is foundational in a number of ways and therefore is a valuable study. The BERT paper 11 Oct 2018| blog post 2 Nov 2018| source code So many tasty layers, | medium | 1,548 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.