New Center at University of Michigan Aims to Democratize the Design and Manufacturing of Next-Generation Systems

January 15, 2018

ANN ARBOR, Mich., Jan. 15, 2018 — As the computing industry struggles to maintain its historically rapid pace of innovation, a new, $32 million center based at the University of Michigan aims to streamline and democratize the design and manufacturing of next-generation computing systems.

The Center for Applications Driving Architectures, or ADA, will develop a transformative, “plug-and-play” ecosystem to encourage a flood of fresh ideas in computing frontiers such as autonomous control, robotics and machine-learning.

Today, analysts worry that the industry is stagnating, caught between physical limits to the size of silicon transistors and the skyrocketing costs and complexity of system design.

“The electronic industry is facing many challenges going forward, and we stand a much better chance of solving these problems if we can make hardware design more accessible to a large pool of talent,” said Valeria Bertacco, an Arthur F. Thurnau professor of computer science and engineering at U-M and director of the ADA Center.  “We want to make it possible for anyone with motivation and a good idea to build novel high-performance computing systems.”

The center is a five-year project that’s led by U-M and includes researchers from a total of seven universities, pending final contracts: Harvard University, MIT, Stanford University, Princeton University, University of Illinois and University of Washington.

ADA is funded by a consortium that is led by the Semiconductor Research Corporation and includes the Defense Advanced Research Projects Agency. The center is one of six new centers recently announced as part of the Joint University Microelectronics Program, organized by the Semiconductor Research Corporation.

ADA aims to democratize the development and deployment of advanced computing systems in several ways: It will develop a modular approach to system hardware and software design, where applications’ internal algorithms are mapped to highly efficient and reusable accelerated hardware components. This faster and more effective approach will require that the entire design framework—from system software to architecture to design tools—be reimagined and rebuilt.

“You shouldn’t need a Ph.D. to design new computing systems,” Bertacco said. “Five years from now, I’d like to see freshly minted college grads doing hardware startups.”

Computing has had a monumental impact on society, but the path forward is uncertain. Researchers are looking for creative approaches to extend the utility of traditional silicon beyond the Moore’s Law era, a long-standing but waning trend in which chips become cheaper to manufacture, and more powerful, each year.

ADA researchers see customized silicon for specific applications—like chips optimized for image search or data analytics—as a promising approach. But the biggest industrial customized silicon successes to date, such as smartphone systems-on-a-chip or graphics processing units, have required the immense resources of large, deeply integrated, vertical design teams. ADA’s goal is to change that. The center is organized into three themes:

Agile system development: The team will identify patterns in the core algorithms of emerging applications—such as virtual reality, machine learning and augmented reality—in order to map those algorithms to new, tailored computational blocks.This approach would slash design costs by building ready-to-use components that usher designs all the way from high-level computational languages to fully packaged systems.

Algorithms-driven architectures: The researchers will develop reusable, highly efficient algorithmic hardware accelerators for the computational blocks. Instead of targeting the application itself, designs will target the underlying algorithms. Special-purpose hardware designs can improve the efficiency-per-operation by several orders of magnitude over a general-purpose chip. Such special-purpose hardware design occurs today, but it can take a decade after a need is identified before mature and efficient solutions are available, and it requires extremely specialized expertise, the researchers say.

Technology-driven systems: A key aspect of this theme involves developing an open-source chip scaffold for these new, accelerator-centric systems. The scaffolds would include all the necessary support subsystems—such as general-purpose cores, on-chip communication fabric, and memories—to facilitate a “plug-and-play” flow. “One will no longer need to send a design to the fab and wait for a chip to come back. They may still need a clean room to assemble a system, but this will be much simpler and more economical,” Bertacco said. Researchers will also explore technology innovations independent of silicon scaling.

“This is a daring and progressive approach to system design that stands to revolutionize the computing industry,” said Alec Gallimore, who is the Robert J. Vlasic Dean of Engineering, the Richard F. and Eleanor A. Towner Professor, an Arthur F. Thurnau Professor, and a professor both of aerospace engineering and of applied physics. “The work of this new center will empower generations of engineers and computer scientists to design and build the systems that can bring their ideas to life.”

DARPA and the Semiconductor Research Corporation will contribute $27.5 million to this project, with the remaining funds provided by the participating institutions. The Semiconductor Research Corporation is a global, high technology-based consortium that serves as a crossroads of collaboration between technology companies, academia, government agencies, and SRC’s engineers and scientists.


Source: University of Michigan

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

Nvidia Appoints Andy Grant as EMEA Director of Supercomputing, Higher Education, and AI

March 22, 2024

Nvidia recently appointed Andy Grant as Director, Supercomputing, Higher Education, and AI for Europe, the Middle East, and Africa (EMEA). With over 25 years of high-performance computing (HPC) experience, Grant brings a Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the fi Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire