This cool looking robot is made using a Raspberry Pi and 3D printed parts. In this project, a dedicated algorithm is made so that the robot can autonomously navigate the track as well as perform tasks such as line following, detecting an obstacle, grabbing and delivering an object. In addition, robustness is also considered it is because if robot navigate the pathway multiple times its performance will not affect. Each Friday is PiDay here at Adafruit! Be sure to check out our posts, tutorials and new Raspberry Pi related products.
LONDON/NEW YORK – Nvidia Corp. is in advanced talks to acquire Arm Ltd., the chip designer that SoftBank Group Corp. bought for $32 billion four years ago, according to people familiar with the matter. The two parties aim to reach a deal in the next few weeks, the people said, asking not to be identified because the information is private. Nvidia is the only suitor in concrete discussions with SoftBank, according to the people. A deal for Arm could be the largest ever in the semiconductor industry, which has been consolidating in recent years as companies seek to diversify and add scale. But any deal with Nvidia, which is a customer of Arm, would likely trigger regulatory scrutiny as well as a wave of opposition from other firms.
The University of Florida and NVIDIA Tuesday unveiled a plan to build the world's fastest AI supercomputer in academia, delivering 700 petaflops of AI performance. The effort is anchored by a $50 million gift: $25 million from alumnus and NVIDIA co-founder Chris Malachowsky and $25 million in hardware, software, training and services from NVIDIA. "We've created a replicable, powerful model of public-private cooperation for everyone's benefit," said Malachowsky, who serves as an NVIDIA Fellow, in an online event featuring leaders from both the UF and NVIDIA. UF will invest an additional $20 million to create an AI-centric supercomputing and data center. The $70 million public-private partnership promises to make UF one of the leading AI universities in the country, advance academic research and help address some of the state's most complex challenges.
The third round of MLPerf training benchmark scores for eight different AI models are out, with rivals Nvidia and Google both staking a claim to the crown. While both companies claimed victory, the results bear further scrutiny. Scores are based on systems, not individual accelerator chips. While Nvidia swept the board for commercially available systems with its Ampere A100-based supercomputer, Google's massive TPU v3 system and smaller TPU v4 systems, which it entered under the research category, makes the search giant a strong contender. Nvida took first place in normalized results for all benchmarks in the commercially available systems category with its A100-based systems.
MLPerf.org released its third round of training benchmark (v0.7) results today and Nvidia again dominated, claiming 16 new records. Meanwhile, Google provided early benchmarks for its next generation TPU 4.0 accelerator and Intel previewed performance on third-gen processors (Cooper Lake). Notably, the MLPerf benchmarking organization continues to demonstrate growth; it now has 70 members, a jump from 40 last July when training benchmarks were last released. Fresh from the launch of its new A100 GPU in May and a top ten finish by Selene (DGX A100 SuperPOD) in June on the most recent Top500 List, Nvidia was able run the MLPerf training benchmarks on its new offerings in time for the July MLPerf release. Impressively, Nvidia set records for scaled out system performance and single node performance (see slides below).
Nvidia and Google on Wednesday each announced that they had aced a series of tests called MLPerf to be the biggest and best in hardware and software to crunch common artificial intelligence tasks. The devil's in the details, but both companies' achievements show the trend in AI continues to be that of bigger and bigger machine learning endeavors, backed by more-brawny computers. Benchmark tests are never without controversy, and some upstart competitors of Nvidia and Google, notably Cerebras Systems and Graphcore, continued to avoid the benchmark competition. In the results announced Wednesday by the MLPerf organization, an industry consortium that administers the tests, Nvidia took top marks across the board for a variety of machine learning "training" tasks, meaning the computing operations required to develop a machine learning neural network from scratch. The full roster of results can be seen in a spreadsheet form.
Layered on top of NVIDIA CUDA, RAPIDS is a suite of open-source software libraries and APIs that provide GPU parallelism and high-bandwidth memory speed through DataFrame and graph operations, achieving speedup factors of 50x or more on typical end-to-end data science workflows. For Spark 3.0, new RAPIDS APIs are used by Spark SQL and DataFrames for GPU accelerated memory efficient columnar data processing and query plans. With Spark 3.0 the Catalyst query optimizer has been modified to identify operators within a query plan that can be accelerated with the RAPIDS API, and to schedule those operators on GPUs within the Spark cluster, when executing the query plan. A new Spark shuffle implementation, built upon GPU accelerated communication libraries including Remote direct memory access (RDMA), dramatically reduces the data transfer among Spark processes. RDMA allows GPUs to communicate directly with each other, across nodes, at up to 100Gb/s, operating as if on one massive server.
Multiple reports yesterday claim that graphics and data center AI silicon powerhouse NVIDIA has expressed interest in acquiring Arm. Arm's Japanese holding company SoftBank has been exploring the potential sale or an IPO of Arm for some time now, more recently courting Apple for a possible deal. Apple reportedly decided not to engage a bid and a Bloomberg source now claims NVIDIA has stepped up with specific interest in a deal. For reference, Arm core processing IP is heavily licensed around the globe and the company's technologies power virtually every smartphone chip on the market, from Apple silicon to Qualcomm, Huawei and others. Arm core processor technologies also power a huge range of connected devices, from the IoT and the connect home, to automotive applications and even supercomputing.
When I was in middle school (quite a few years ago), I started to realize that I was pretty good at math. I had done okay before, but the problems and concepts were becoming more difficult. Surprisingly, I was really "getting it", while some of my classmates encountered more challenges with the subject matter. The teacher motivated us with a special letter grade when our performance on a homework assignment or quiz was stellar -- a large letter grade "A" on the top of our assignment page, which she called a "big bold A". This grade was not simply recognition for getting a 90% score on the assignment, but was awarded for achieving 99% or 100%.
The NVIDIA DGX A100 is a high-performance computing system for AI training, inference and analytics. It sets a new bar for compute density, packing 5 petaFLOPS of AI performance into a 6U form factor, replacing legacy infrastructure silos with one flexible platform that can support every AI workload. "With the NVIDIA DGX A100, NVIDIA has really changed the game for AI in terms of extreme performance, scale and flexibility. By offering colocation services and flexible lease options, we're making this technology more accessible than ever before," said Jason Chen, Vice President of Exxact Corporation. More than just a server, the DGX A100 integrates exclusive access to the Exxact team of AI-fluent experts that offer prescriptive planning, deployment, and optimization expertise to help fast-track AI transformation. Available now, the NVIDIA DGX A100 can be bundled with an optional three-year warranty and support package to improve productivity by reducing downtime on production systems.