Adapteva Unveils 64-Core Chip

By Michael Feldman

August 22, 2012

Chipmaker Adapteva is sampling its 4th-generation multicore processor, known as Epiphany-IV. The 64-core chip delivers a peak performance of 100 gigaflops and draws just two watts of power, yielding a stunning 50 gigaflops/watt. The engineering samples were manufactured by GLOBALFOUNDRIES on its latest 28nm process technology.

Based in LEXINGTON, Massachusetts, Adapteva is in the business of developing ultra-efficient floating point accelerators. Andreas Olofsson, a former chip engineer at Texas Instruments and Analog Devices, founded the company in 2008, and gathered $2.5 million from various VCs and private investors. With that shoestring budget, he managed to produce four generations of the Epiphany architecture, including two actual chips. The technology is initially aimed at the mobile and embedded market, but Olofsson also has designs on penetrating the supercomputing space.

Epiphany is essentially a stripped down general-purpose RISC CPU that throws out almost everything but the number-crunching silicon. But since it doesn’t incorporate features needed by operating systems, like memory management, it relies on a host processor to feed it application kernels in the same manner as a GPGPU. The current implementation supports single precision floating point only, but plans are already in the works for a double precision implementation.

The general layout of Epiphany is a 2D mesh of simple cores, which talk to each other via a high-speed interconnect.  In that sense, it looks more like Intel’s manycore Xeon Phi than a graphics processor, but without the x86 ISA baggage (but also without the benefit of the x86 ecosystem).

The latest Epiphany chip, which was spec’d out last fall, runs at a relatively slow 800MHz.  But thanks to its highly parallel design and simplified cores, its 50 gigaflops/watt energy efficiency is among the best in the business. NVIDIA’s new K10 GPU computing card can hit about 20 single precision gigaflops/watt, but that also includes 8GB of GDDR5 memory and a few other on-board components, so it’s not an apples-to-apples comparison. Regardless, a 100 gigaflop chip drawing a couple of watts is a significant achievement.

The downside of the design is that it uses Adapteva’s own proprietary ISA, so there are no ready-made software tools that developers can tap into. “Everybody is very impressed by the numbers,” Olofsson told HPCwire. “They just haven’t quite been convinced they can program this thing.”

That has now changed.  In conjunction with the 28nm samples, Adapteva has also released its own OpenCL compiler wrapped in their new software developer kit (SDK). The compiler is an adaptation of Brown Deer Technology’s OpenCL implementation developed for ARM and x86 platforms. Brown Deer provides tools and support for high performance computing applications and is especially focused on acceleration technologies based on GPUs and FPGAs. The Adapteva implementation means developers can now use standard OpenCL source to program the Epiphany processor.
Olofsson says they chose OpenCL because it’s a recognized open standard that is being used for heterogeneous computing platforms in all the segments Adapteva is interested in. In particular, it’s getting some traction on heterogeneous platforms in the embedded space, where GPUs are increasingly being targeted to general-purpose computing.  “The way we are pitching [Epiphany] is that OpenCL GPGPUs may not be good at everything, because of their architectural limitations,” say Olofsson. “So why not put another accelerator next to it that is also OpenCL-programmable.”  

Adapteva is putting the SDK through its paces using existing OpenCL codes like 2D Fast Fourier Transform (FFT) and multi-body physics algorithms that were downloaded off the Internet. The company is currently using an x86-based board for these test runs, but since OpenCL has bindings for C/C++, essentially any commodity CPU is fair game as the host driver. Adapteva’s SDK is currently in beta form and is being released to the company’s early access partners.

As far as getting the Epiphany chips onto useful platforms, that’s still a work in progress. At least some of the engineering samples of the 28nm chip will go to Bittware, an early customer of Adapteva’s. Bittware used the early 16-core, 32-gigaflop version of Epiphany on its custom PCIe boards.  Those products are aimed at military and industrial application for things like embedded signal processing. Because of the need to minimize power usage in embedded computing, Epiphany is a good fit for this application domain.  At least one more vendor has signed up to develop Epiphany-based PCIe boards, but that company is not ready to go public just yet.

Adapteva’s market aspirations extend beyond the military-industrial complex though. Olofsson believes Epiphany is ideal for mobile computing, and eventually HPC.  With regard to the former, Adapteva is planning to use the new chip to demonstrate face detection, an application aimed at devices like smartphones and tablets. Face detection and recognition rely on very compute-intensive algorithms, which is fine if you’ve got a server or two to spare, but it’s beyond the number-crunching capabilities of most mobile-grade CPUs and GPUs today.

Other flop-hungry applications that could find a home on in this market include augmented overlays, gesture recognition, real-time speech recognition, realistic game physics, and computational photography. Like mobile-based face detection/recognition, all of these require lots of computational performance operating within very restricted power envelopes.

For high performance computing, the path is a little more complex. For starters, someone has to build a Epiphany-based PCIe card suitable for HPC servers, and then an OEM has to be enticed to support that board. To deliver a reasonable amount of computation for  a server — say, a teraflop or so — you would need multiple Epiphany chips glued to a card, which would necessitate a PCIe expansion setup of some sort. Not an impossibility, but probably not a job for a do-it-yourselfer.

More fundamentally though, the architecture has to add support for double precision floating point to be taken seriously for HPC (although applications like seismic modeling, image and audio processing, and video analysis are fine with single precision).  
In any case, double precision is already on Adapteva’s roadmap. “We’ll definitely have something next year,” says Olofsson.

Beyond that, the company has plans on the drawing board to scale this architecture up to the teraflop/watt realm. Following a Moore’s Law trajectory, that would mean that by 2018 a 7nm Epiphany processor could house 1,000 cores and deliver a whopping two teraflops.  Since such a chip would draw the same two watts as the current 100 gigaflops version, it could easily provide the foundation for an exascale supercomputer. Or a killer tablet.

 


 

Related Articles

Adapteva Builds Manycore Processor That Will Deliver 70 Gigaflops/Watt

Startup Launches Manycore Floating Point Acceleration Technology

 

 

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!

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion XL — were added to the benchmark suite as MLPerf continues 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 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…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion 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…

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