Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

By Alex Woodie

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of a new generation of chips designed specifically for deep learning workloads.

Google has been using its TPUs for the inference stage of a deep neural network since 2015. It credits the TPU for helping to bolster the effectiveness of various artificial intelligence workloads, including language translation and image recognition programs. It also says TPU helped power its widely reported victory in the game of Go.

While TPUs aren’t new to Google data centers, the company started talking about them publicly only recently. Earlier this month, the Alphabet subsidiary opened up about the TPU, which it called “our first machine learning chip,” in a blog post. The company also released a technical paper, titled “In-Datacenter Performance Analysis of a Tensor Processing Unit​,” that details the design and performance characteristics of the TPU.

According to the paper, Google’s TPU was 15 to 30 times faster at inference than Nvidia’s K80 GPU and Intel Haswell CPU in a Google benchmark test. On a performance per watt scale, the TPUs are 30 to 80 times more efficient than the CPU and GPU (with the caveat that these are older designs). You can read more details on the TPU comparisons here.

While Google has been mum on possible commercial ventures around the TPU, some recent developments indicate that Google itself may not be aiming to compete directly with traditional chip manufacturers. Last week CNBC reported that a group of the original Google engineers who designed the TPU recently left the Web giant to found their own company, called Groq.

Google’s TPU chip (Source: Google)

According to an SEC document filed for Groq’s incorporation, the company has raised about $10 million. Leading the way is Chamath Palihapitiy, a prominent Silicon Valley venture capitalist. Other ex-Googlers named in the SEC document include Jonathan Ross, who helped invent the TPU, and Douglas Wightman, who worked on the Google X “moonshot factory.”

But that’s not all. “We have eight of the 10 original people that built that chip building the next generation chip now,” Palihapitiy said in a March interview with CNBC. Groq is playing its cards close to the vest, and isn’t disclosing exactly what it’s working on—although by all indications, it would appear to have something to do with machine learning chips.

There are many other groups chasing this new market opportunity, including traditional chip bigwigs Intel and IBM.

While Big Blue pushes a combination of its RISC Power chips and Nvidia GPUs in its Minsky AI server, its research arm is exploring other chip architectures. Most recently, the company’s Almaden Lab has discussed the capabilities of its “brain-inspired” TrueNorth chip, which features 1 million neurons and 256 million synapses. IBM says TrueNorth has delivered “deep networks that approach state-of-the-art classification accuracy” on several vision and speech datasets.

“The goal of brain-inspired computing is to deliver a scalable neural network substrate while approaching fundamental limits of time, space, and energy,” IBM Fellow Dharmendra Modha, chief scientist of Brain-inspired Computing at IBM Research, said in a blog post.

Intel isn’t standing still, and is developing its own chip architectures for next-generation AI workloads. Last year the company announced that its first AI-specific hardware, code-named “Lake Crest,” which is based on technology Intel acquired with $400-million acquisition of Nervana Systems, would debut in the first half of 2017. That is to be followed later this year with Knights Mill, the next iteration of its Xeon Phi co-processor architecture.

IBM’s TrueNorth training set (image source: IBM Research)

For its part, Nvidia will be looking to solidify its hold on the emerging machine learning market. While energy-hungry GPUs aren’t as efficient on the inference side of the equation, they’re tough to be beat for the compute-intensive training of neural networks, which is why Web giants like Google, Facebook, Microsoft and others are using so many of them for AI workloads.

However, Nvidia isn’t giving up on the inference side of the market, and recently published a benchmark that showed how much better its latest Pascal GPU architectures, most notably the P40, is at inferring than its older Kepler GPU architecture (see HPCwire’s coverage here). The K80 also out-performed the Google TPU, although Google has probably advanced its TPU since 2015, which is when it calculated the benchmark figures it recently shared. Nvidia’s recent hiring of Clément Farabet (formerly of Twitter) also could also portend a shift to more real-time workloads too.

Qualcomm could also be involved in the inference side of the equation. The mobile chipmaker has been working with Yann LeCun, Facebook’s Director of AI Research, to develop new chips for real-time inference, according to this Wired story. LeCun developed one of the first AI-specific chips for inference more than 25 years ago while working at Bell Labs.

The San Diego company recently announced plans to spend $47 billion to buy NXP, a Dutch company that makes chips for cars. NXP was working on deep learning and computer vision problems before the acquisition was announced, and it appears that Qualcomm will be looking to NXP to give it an edge in developing systems for autonomous driving.

Self-driving cars are one of the most prominent areas where deep learning and AI will have an impact. Beyond that, there are many other places where having an on-board AI chip to react to real-world conditions, including in mobile phones and virtual reality headsets. The technology is moving very quickly at the moment, and we’ll soon see other practical uses that will impact our lives.

This article first appeared on our sister site, Datanami.

Subscribe to HPCwire's Weekly Update!

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

Cray+Azure: Can Cloud Propel Supercomputing?

October 23, 2017

Cray and Microsoft today announced they will offer dedicated Cray supercomputers (the XC and CS-Storm lines) inside the Azure platform allowing customers to run their HPC and AI applications alongside their other cloud w Read more…

By Tiffany Trader

2017 Gordon Bell Prize Finalists Named

October 23, 2017

The three finalists for this year’s Gordon Bell Prize in High Performance Computing have been announced. They include two papers on projects run on China’s Sunway TaihuLight system and a third paper on 3D image recon Read more…

By John Russell

Data Vortex Users Contemplate the Future of Supercomputing

October 19, 2017

Last month (Sept. 11-12), HPC networking company Data Vortex held its inaugural users group at Pacific Northwest National Laboratory (PNNL) bringing together about 30 participants from industry, government and academia t Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Transforming Genomic Analytics with HPC-Accelerated Insights

Advancements in the field of genomics are revolutionizing our understanding of human biology, rapidly accelerating the discovery and treatment of genetic diseases, and dramatically improving human health. Read more…

AI Self-Training Goes Forward at Google DeepMind

October 19, 2017

DeepMind, Google’s AI research organization, announced today in a blog that AlphaGo Zero, the latest evolution of AlphaGo (the first computer program to defeat a Go world champion) trained itself within three days to play Go at a superhuman level (i.e., better than any human) – and to beat the old version of AlphaGo – without leveraging human expertise, data or training. Read more…

By Doug Black

Cray+Azure: Can Cloud Propel Supercomputing?

October 23, 2017

Cray and Microsoft today announced they will offer dedicated Cray supercomputers (the XC and CS-Storm lines) inside the Azure platform allowing customers to run Read more…

By Tiffany Trader

Data Vortex Users Contemplate the Future of Supercomputing

October 19, 2017

Last month (Sept. 11-12), HPC networking company Data Vortex held its inaugural users group at Pacific Northwest National Laboratory (PNNL) bringing together ab Read more…

By Tiffany Trader

AI Self-Training Goes Forward at Google DeepMind

October 19, 2017

DeepMind, Google’s AI research organization, announced today in a blog that AlphaGo Zero, the latest evolution of AlphaGo (the first computer program to defeat a Go world champion) trained itself within three days to play Go at a superhuman level (i.e., better than any human) – and to beat the old version of AlphaGo – without leveraging human expertise, data or training. Read more…

By Doug Black

Student Cluster Competition Coverage New Home

October 16, 2017

Hello computer sports fans! This is the first of many (many!) articles covering the world-wide phenomenon of Student Cluster Competitions. Finally, the Student Read more…

By Dan Olds

Intel Delivers 17-Qubit Quantum Chip to European Research Partner

October 10, 2017

On Tuesday, Intel delivered a 17-qubit superconducting test chip to research partner QuTech, the quantum research institute of Delft University of Technology (TU Delft) in the Netherlands. The announcement marks a major milestone in the 10-year, $50-million collaborative relationship with TU Delft and TNO, the Dutch Organization for Applied Research, to accelerate advancements in quantum computing. Read more…

By Tiffany Trader

Fujitsu Tapped to Build 37-Petaflops ABCI System for AIST

October 10, 2017

Fujitsu announced today it will build the long-planned AI Bridging Cloud Infrastructure (ABCI) which is set to become the fastest supercomputer system in Japan Read more…

By John Russell

HPC Chips – A Veritable Smorgasbord?

October 10, 2017

For the first time since AMD's ill-fated launch of Bulldozer the answer to the question, 'Which CPU will be in my next HPC system?' doesn't have to be 'Whichever variety of Intel Xeon E5 they are selling when we procure'. Read more…

By Dairsie Latimer

Delays, Smoke, Records & Markets – A Candid Conversation with Cray CEO Peter Ungaro

October 5, 2017

Earlier this month, Tom Tabor, publisher of HPCwire and I had a very personal conversation with Cray CEO Peter Ungaro. Cray has been on something of a Cinderell Read more…

By Tiffany Trader & Tom Tabor

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

NERSC Scales Scientific Deep Learning to 15 Petaflops

August 28, 2017

A collaborative effort between Intel, NERSC and Stanford has delivered the first 15-petaflops deep learning software running on HPC platforms and is, according Read more…

By Rob Farber

Oracle Layoffs Reportedly Hit SPARC and Solaris Hard

September 7, 2017

Oracle’s latest layoffs have many wondering if this is the end of the line for the SPARC processor and Solaris OS development. As reported by multiple sources Read more…

By John Russell

US Coalesces Plans for First Exascale Supercomputer: Aurora in 2021

September 27, 2017

At the Advanced Scientific Computing Advisory Committee (ASCAC) meeting, in Arlington, Va., yesterday (Sept. 26), it was revealed that the "Aurora" supercompute Read more…

By Tiffany Trader

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

GlobalFoundries Puts Wind in AMD’s Sails with 12nm FinFET

September 24, 2017

From its annual tech conference last week (Sept. 20), where GlobalFoundries welcomed more than 600 semiconductor professionals (reaching the Santa Clara venue Read more…

By Tiffany Trader

Google Releases Deeplearn.js to Further Democratize Machine Learning

August 17, 2017

Spreading the use of machine learning tools is one of the goals of Google’s PAIR (People + AI Research) initiative, which was introduced in early July. Last w Read more…

By John Russell

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Leading Solution Providers

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in Read more…

By Tiffany Trader

Amazon Debuts New AMD-based GPU Instances for Graphics Acceleration

September 12, 2017

Last week Amazon Web Services (AWS) streaming service, AppStream 2.0, introduced a new GPU instance called Graphics Design intended to accelerate graphics. The Read more…

By John Russell

EU Funds 20 Million Euro ARM+FPGA Exascale Project

September 7, 2017

At the Barcelona Supercomputer Centre on Wednesday (Sept. 6), 16 partners gathered to launch the EuroEXA project, which invests €20 million over three-and-a-half years into exascale-focused research and development. Led by the Horizon 2020 program, EuroEXA picks up the banner of a triad of partner projects — ExaNeSt, EcoScale and ExaNoDe — building on their work... Read more…

By Tiffany Trader

Delays, Smoke, Records & Markets – A Candid Conversation with Cray CEO Peter Ungaro

October 5, 2017

Earlier this month, Tom Tabor, publisher of HPCwire and I had a very personal conversation with Cray CEO Peter Ungaro. Cray has been on something of a Cinderell Read more…

By Tiffany Trader & Tom Tabor

Cray Moves to Acquire the Seagate ClusterStor Line

July 28, 2017

This week Cray announced that it is picking up Seagate's ClusterStor HPC storage array business for an undisclosed sum. "In short we're effectively transitioning the bulk of the ClusterStor product line to Cray," said CEO Peter Ungaro. Read more…

By Tiffany Trader

Intel Launches Software Tools to Ease FPGA Programming

September 5, 2017

Field Programmable Gate Arrays (FPGAs) have a reputation for being difficult to program, requiring expertise in specialty languages, like Verilog or VHDL. Easin Read more…

By Tiffany Trader

IBM Advances Web-based Quantum Programming

September 5, 2017

IBM Research is pairing its Jupyter-based Data Science Experience notebook environment with its cloud-based quantum computer, IBM Q, in hopes of encouraging a new class of entrepreneurial user to solve intractable problems that even exceed the capabilities of the best AI systems. Read more…

By Alex Woodie

HPC Chips – A Veritable Smorgasbord?

October 10, 2017

For the first time since AMD's ill-fated launch of Bulldozer the answer to the question, 'Which CPU will be in my next HPC system?' doesn't have to be 'Whichever variety of Intel Xeon E5 they are selling when we procure'. Read more…

By Dairsie Latimer

  • arrow
  • Click Here for More Headlines
  • arrow
Share This